Amodal segmentation is an image-based algorithm that aims to predict masks for both visible and occluded parts of objects. Existing methods typically rely on supervised learning with annotated amodal masks or synthetic data. The effectiveness of these methods relies heavily on the quality of the datasets. This dependence can unintentionally restrict their generalization capabilities due to insufficient diversity and size. Although existing zero-shot methods perform well on their reported datasets, their performance does not necessarily transfer to other datasets. We propose a tuning-free approach that re-purposes diffusion-based inpainting foundation models for amodal segmentation. Our approach is motivated by the “occlusion-free bias” of inpainting models, i.e., the inpainted objects tend to be complete and without occlusions. We reconstruct the occluded regions of an object via inpainting and then apply segmentation, all without additional training or fine-tuning. Experiments on five datasets, three previously unreported, demonstrate the generalizability of our approach. On average, our approach achieves 5.3% more accurate masks in mIoU compared to the publicly available state-of-the-art, pix2gestalt.

Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014–2025). We coded each study’s approach, learning context, skill level, modality, pedagogy, and outcomes (κ =.918). Three patterns emerged: (1) approach–context–pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p <.001] and favored project-based learning [p =.009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

Fungal wood decay is a complex biophysical phenomenon that involves the degradation of a variety of structural wood components, ranging from lignin and carbohydrates to defensive chemical agents. All these substrates serve as varying resources with different material properties that determine the rate of fungal propagation and the structural integrity and color of decaying wood. We propose a novel approach to simulate the dynamic interactions between the biological and mechanical components of wood decay, including fungal colonization, chemical defense, and moisture-driven fracture. We propose a novel volumetric representation of trees that includes grain-aligned mesh generation, internal moisture dynamics, and tissue-specific health states. Furthermore, we model the anisotropic diffusion, consumption, and resulting material failure caused by white and brown rot fungi. This allows simulating and rendering 3D volumetric decaying trees that realistically capture key aspects of the process, such as the progression of cuboid fracture patterns, the hollowing of trunks, and the effects of environmental moisture on structural stability.

This paper describes XRXL, an extended-reality system for increasing student engagement in large lectures. Students wear XR headsets to see 3D visualizations controlled by the instructor. The instructor can virtually retract the roof and walls of the classroom to allow for large-scale visualizations that extend beyond the physical boundaries of the classroom, or to turn the classroom into a 360° theater. The instructor can also partition the classroom into small groups of students and to assist individual groups as needed. XRXL was tested in an IRB-approved user study with 82 students in the context of a mock-lecture on neural networks. To the best of our knowledge, the study is the largest deployment of a co-located collaborative XR application to date. The study shows that students had a favorable opinion of XRXL, that XRXL had a low task load, an acceptable usability level, and that it did not cause cybersickness.

We introduce RGB2Point, an unposed single-view RGB image to a 3D point cloud generation based on Transformer. RGB2Point takes an input image of an object and generates a dense 3D point cloud. Contrary to prior works based on CNN layers and diffusion-denoising approaches, we use pretrained Transformer layers that are fast and generate high-quality point clouds with consistent quality over available categories. Our generated point clouds demonstrate high quality on a real-world dataset, as evidenced by improved Chamfer distance (51.15%) and Earth Mover's distance (36.17%) metrics compared to the current state-of-the-art. Additionally, our approach shows a better quality on a synthetic dataset, achieving better Chamfer distance (39.26%), Earth Mover's distance (26.95%), and F-score (47.16%). Moreover, our method produces 63.1% more consistent high-quality results across various object categories compared to prior works. Furthermore, RGB2Point is computationally efficient, requiring only 2.3GB of VRAM to reconstruct a 3D point cloud from a single RGB image, and our implementation generates the results 15,133 x faster than a SOTA diffusion-based model.

Abstract Terrain generation is a challenging problem. Procedural modelling methods lack control, while machine learning methods often need large training datasets and struggle to preserve the topology information. We propose a method that generates a new terrain from a single image for training and a simple user sketch. Our single‐shot method preserves the sketch topology while generating diversified results. Our method is based on a graph neural network (GNN) and builds a detailed relation among the sketch‐extracted features, that is, ridges and valleys and their neighbouring area. By disentangling the influence from different sketches, our model generates visually realistic terrains following the user sketch while preserving the features from the real terrains. Experiments are conducted to show both qualitative and quantitative comparisons. The structural similarity index measure of our generated and real terrains is around 0.8 on average.

We introduceTreeStructor, a novel approach for isolating and reconstructing forest trees. The key novelty is a deep neural model that uses neural ranking to assign pre-generated connectable 3D geometries to a point cloud.TreeStructoris trained on a large set of synthetically generated point clouds. The input to our method is a forest point cloud (FPC) that we first decompose into point clouds that approximately represent trees (TPC) and then into point clouds that represent their parts (PPC). We use a point cloud encoder-decoder to compute embedding vectors that retrieve the best-fitting surface mesh for eachPPCfrom a large set of predefined branch parts. Finally, the retrieved meshes are connected and oriented to obtain individual surface meshes of all trees represented by theFPC. We qualitatively and quantitatively validate that our method can reconstruct forest trees with unprecedented accuracy and visual fidelity.TreeStructoroutperforms the state-of-the-art reconstruction method for around 6% on quantitative metrics and 12% less error compared with QSM on low-quality scanned data. The code and data are available at https://lewkesy.github.io/TreeStructor/.

Presents corrections to the paper, (Errata to “TreeStructor: Forest Reconstruction With Neural Ranking”).

We introduce Arenite, a novel physics-based approach for modeling sandstone structures. The key insight of our work is that simulating a combination of stress and multi-factor erosion enables the generation of a wide variety of sandstone structures observed in nature. We isolate the key shape-forming phenomena: multi-physics fabric interlocking, wind and fluvial erosion, and particle-based deposition processes. Complex 3D structures such as arches, alcoves, hoodoos, or buttes can be achieved by creating simple 3D structures with user-painted erodable areas and vegetation and running the simulation. We demonstrate the algorithm on a wide variety of structures, and our GPU-based implementation achieves the simulation in less than 5 minutes on a desktop computer for our most complex example.

This paper introduces SfmCAD, a novel unsupervised network that reconstructs 3D shapes by learning the Sketchbased Feature Modeling operations commonly used in modern CAD workflows. Given a 3D shape represented as voxels, SfmCAD learns a neural-typed sketch+path parameterized representation, including 2D sketches of feature primitives and their 3D sweeping paths without supervision, for inferring feature-based CAD programs. SfmCAD employs 2D sketches for local detail representation and 3D paths to capture the overall structure, achieving a clear separation between shape details and structure. This conversion into parametric forms enables users to seamlessly adjust the shape's geometric and structural features, thus enhancing interpretability and user control. We demonstrate the effectiveness of our method by applying SfmCAD to many different types of objects, such as CAD parts, ShapeNet objects, and tree shapes. Extensive comparisons show that SfmCAD produces compact and faithful 3D reconstructions with superior quality compared to alternatives. The code is released at https://github.com/BunnySoCrazy/SfmCAD.

Efficiently representing and reconstructing the 3D geometry of biological trees remains a challenging problem in computer vision and graphics. We propose a novel approach for generating realistic tree models from single-view photographs. We cast the 3D information inference problem to a semantic voxel diffusion process, which converts an input image of a tree to a novel Semantic Voxel Structure (SVS) in 3D space. The SVS encodes the geometric appearance and semantic structural information (e.g., classifying trunks, branches, and leaves), which retains the intricate internal tree features. Tailored to the SVS, we present SVDTree a new hybrid tree modeling approach by combining structure-oriented branch reconstruction and self-organization-based foliage reconstruction. We validate SVDTree by using images from both synthetic and real trees. The comparison results show that our approach can better preserve tree details and achieve more realistic and accurate reconstruction results than previous methods.

We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However, existing scene-level datasets for deep learning-based 3D vision, limited to ei-ther synthetic environments or a narrow selection of real-world scenes, are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap, we present DL3DV-10K, a large-scale scene dataset, featuring 51.2 million frames from 10,510 videos captured from 65 types of point- of-interest (POI) locations, covering both bounded and unbounded scenes, with different levels of reflection, transparency, and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K, which revealed valuable insights for future research in NVS. In addition, we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K, which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset, benchmark results, and models will be publicly accessible.

Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods can simulate bokeh effects without relying on a physical camera lens, but the inaccurate lens modeling in existing filtering-based meth-ods leads to artifacts that need post-processing or learning-based methods to fix. We propose Dr.Bokeh, a novel ren-dering method that addresses the issue by directly correcting the defect that violates physics in the current filtering-based bokeh rendering equation. Dr.Bokeh first preprocesses the input RGBD to obtain a layered scene representation. Dr.Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur based on the novel occlusion-aware bokeh rendering method. Experiments show that the non-learning based renderer Dr.Bokeh outperforms state-of-the-art bokeh ren-dering algorithms in terms of photo-realism. In addition, extensive quantitative and qualitative evaluations show that the more accurate lens model pushes the limit of depth-from-defocus.

Researchers have provided insights into using virtual reality (VR) for visualization and interaction with 3D models and simulations. The interaction allows users to manipulate the 3D elements and visualize changes based on their inputs from movement with controllers or spatial actions. However, some users may find this interaction overwhelming, especially when immersed in a virtual environment. Additionally, the choice of dimensionality for visualizations influences user interaction, with potential implications for immersive experiences. Thus, we conducted a 2 (Immersion: Desktop vs. HMDVR) $\times 2$ (Dimensionality: 2 D vs. 3 D) within-group study $(N=32)$ to explore the impact of the utilized immersive degree and the dimensionality representation of the content on participants’ experience in terms of engagement, task load, usability, skill, and emotions when interacting with a science simulation. We designed and developed an application to simulate charged particles and electric field lines. We asked participants to complete a task of changing particles by matching them to a given simulation output. Our results indicated higher workload rates for HMDVR conditions, particularly with 3D representation, compared to Desktop. However, HMDVR conditions also showed greater engagement, emotional response, and presence. Based on our findings, we argue that participants prefer HMDVR over Desktop environments regardless of dimensionality.

Abstract While the past of terrain cannot be known precisely because an effect can result from many different causes, exploring these possible pasts opens the way to numerous applications ranging from movies and games to paleogeography. We introduce unerosion, an attempt to recover plausible past topographies from an input terrain represented as a height field. Our solution relies on novel algorithms for the backward simulation of different processes: fluvial erosion, sedimentation, and thermal erosion. This is achieved by re‐formulating the equations of erosion and sedimentation so that they can be simulated back in time. These algorithms can be combined to account for a succession of climate changes backward in time, while the possible ambiguities provide editing options to the user. Results show that our solution can approximately reverse different types of erosion while enabling users to explore a variety of alternative pasts. Using a chronology of climatic periods to inform us about the main erosion phenomena, we also went back in time using real measured terrain data. We checked the consistency with geological findings, namely the height of river beds hundreds of thousands of years ago.

Erosion simulation is a common approach used for generating and authoring mountainous terrains. While water is considered the primary erosion factor, its simulation fails to capture steep slopes near the ridges. In these low-drainage areas, erosion is often approximated with slope-reducing erosion, which yields unrealistically uniform slopes. However, geomorphology observed that another process dominates the low-drainage areas: erosion by debris flow, which is a mixture of mud and rocks triggered by strong climatic events. We propose a new method to capture the interactions between debris flow and fluvial erosion thanks to a new mathematical formulation for debris flow erosion derived from geomorphology and a unified GPU algorithm for erosion and deposition. In particular, we observe that sediment and debris deposition tend to intersect river paths, which motivates the design of a new, approximate flow routing algorithm on the GPU to estimate the water path out of these newly formed depressions. We demonstrate that debris flow carves distinct patterns in the form of erosive scars on steep slopes and cones of deposited debris competing with fluvial erosion downstream.

We show how a Transformer can encode hierarchical tree-like string structures by introducing a new deep learning-based framework for generating 3D biological tree models represented as Lindenmayer system (L-system) strings. L-systems are string-rewriting procedural systems that encode tree topology and geometry. L-systems are efficient, but creating the production rules is one of the most critical problems precluding their usage in practice. We substitute the procedural rules creation with a deep neural model. Instead of writing the rules, we train a deep neural model that produces the output strings. We train our model on 155k tree geometries that are encoded as L-strings, de-parameterized, and converted to a hierarchy of linear sequences corresponding to branches. An end-to-end deep learning model with an attention mechanism then learns the distributions of geometric operations and branches from the input, effectively replacing the L-system rewriting rule generation. The trained deep model generates new L-strings representing 3D tree models in the same way L-systems do by providing the starting string. Our model allows for the generation of a wide variety of new trees, and the deep model agrees with the input by 93.7% in branching angles, 97.2% in branch lengths, and 92.3% in an extracted list of geometric features. We also validate the generated trees using perceptual metrics showing 97% agreement with input geometric models.

Generating realistic models of trees and plants is a complex problem because of the vast variety of shapes trees can form. Procedural modeling algorithms are popular for defining branching structures and steadily increasing their expressive power by considering more biological findings. Most existing methods focus on defining the branching structure of trees based on skeletal graphs, while the surface mesh of branches is most commonly defined as simple cylinders. One critical open problem is defining and controlling the complex details observed in real trees. This paper aims to advance tree modeling by proposing a strand-based volumetric representation for tree models. Strands are fixed-size volumetric pipes that define the branching structure. By leveraging strands, our approach captures the lateral development of trees. We combine the strands with a novel branch development formulation that allows us to locally inject vigor and reshape the tree model. Moreover, we define a set of editing operators for tree primary and lateral development that enables users to interactively generate complex tree models with unprecedented detail with minimal effort.

We introduce a novel co-design method for autonomous moving agents' shape attributes and locomotion by combining deep reinforcement learning and evolution with user control. Our main inspiration comes from evolution, which has led to wide variability and adaptation in Nature and has significantly improved design and behavior simultaneously. Our method takes an input agent with optional user-defined constraints, such as leg parts that should not evolve or are only within the allowed ranges of changes. It uses physics-based simulation to determine its locomotion and finds a behavior policy for the input design that is used as a baseline for comparison. The agent is randomly modified within the allowed ranges, creating a new generation of several hundred agents. The generation is trained by transferring the previous policy, which significantly speeds up the training. The best-performing agents are selected, and a new generation is formed using their crossover and mutations. The next generations are then trained until satisfactory results are reached. We show a wide variety of evolved agents, and our results show that even with only 10% of allowed changes, the overall performance of the evolved agents improves by 50%. If more significant changes to the initial design are allowed, our experiments' performance will improve even more to 150%. Our method significantly improved motion tasks without changing body structures, and it does not require considerable computation resources as it works on a single GPU and provides results by training thousands of agents within 30 minutes.

In this article, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model "situated latent" because its behavior is determined by the intrinsic state -encoded as a latent space of a deep neural model- and by the extrinsic (environmental) data that is "situated" as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.

We present a novel approach to perform instance segmentation and counting for densely packed self-similar trees using a top-view RGB image sequence. We propose a solution that leverages pixel content, shape, and self-occlusion. First, we perform an initial over-segmentation of the image sequence and aggregate structural characteristics into a contour graph with temporal information incorporated. Second, using a graph convolutional network and its inherent local messaging passing abilities, we merge adjacent tree crown patches into a final set of tree crowns. Per various studies and comparisons, our method is superior to all prior methods and results in high-accuracy instance segmentation and counting despite the trees being tightly packed. Finally, we provide various forest image sequence datasets suitable for subsequent benchmarking and evaluation captured at different altitudes and leaf conditions.

Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections. However, the lack of geometry limits the quality of the generated soft shadows and constrains reflections to pure specular ones. We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel height representation to 3D space. Using this mapping, PixHt- Lab reconstructs both the cutout and background geometry and renders realistic, diverse lighting effects for image compositing. Given a surface with physically-based materials, we can render reflections with varying glossiness. To generate more realistic soft shadows, we further propose using 3D-aware buffer channels to guide a neural renderer. Both quantitative and qualitative evaluations demonstrate that PixHt-Lab significantly improves soft shadow generation. Project: https://shengcn.github.io/PixHtLab/

This paper proposes a dynamic Knowledge, Skills, and Abilities (KSA) matrix-based taxonomy for the Industry 4.0 workforce. The study methodology consisted firstly of identifying the KSAs through a literature review and secondly of a KSA relevance analysis using information from World Economic Forum (WEF) global reports and the Organization for Economic Cooperation and Development (OECD). Finally, we identified the correlation coefficients of the KSA matrix elements concerning the data on jobs and occupations using information from the European Skills, Competencies, and Occupations (ESCO), Occupational Information Network (O*NET), and the strategic intelligence platform of the World Economic Forum. One of the goals was to make the taxonomy compatible with existing and future machine learning methods (i.e., AI-ready) that will enable efficient and effective use of AI in mining and explaining existing and potentially proposing novel trends and strategies. Preliminary results show that the KSA Industry 4.0 Taxonomy can serve as an international reference guide for designing 2030 educational approaches to active and experiential learning in Higher Education Institutions.

Abstract Various terrain modelling methods have been proposed for the past decades, providing efficient and often interactive authoring tools. However, they seldom include any notion of style, which is critical for designers in the entertainment industry. We introduce a new generative network method that bridges the gap between automatic terrain synthesis and authoring, providing a versatile set of authoring tools allowing spatialised style. We build upon the StyleGAN2 architecture and extend it with authoring tools. Given an input sketch or existing elevation map, our method generates a terrain with features that can be authored, enhanced, and augmented using interactive brushes and style manipulation tools. The strength of our approach lies in the versatility and interoperability of the different tools. We validate our method quantitatively with drainage calculation against other previous techniques and qualitatively by asking users to follow a prompt or freely create a terrain.

We introduce the first solution for simulating the formation and evolution of glaciers, together with their attendant erosive effects, for periods covering the combination of glacial and inter-glacial cycles. Our efficient solution includes both a fast yet accurate deep learning-based estimation of highorder ice flows and a new, multi-scale advection scheme enabling us to account for the distinct time scales at which glaciers reach equilibrium compared to eroding the terrain. We combine the resulting glacial erosion model with finer-scale erosive phenomena to account for the transport of debris flowing from cliffs. This enables us to model the formation of terrain shapes not previously adequately modeled in Computer Graphics, ranging from U-shaped and hanging valleys to fjords and glacial lakes.

Computer graphics has dedicated a considerable amount of effort to generating realistic models of trees and plants. Many existing methods leverage procedural modeling algorithms - that often consider biological findings - to generate branching structures of individual trees. While the realism of tree models generated by these algorithms steadily increases, most approaches neglect to model the root system of trees. However, the root system not only adds to the visual realism of tree models but also plays an important role in the development of trees. In this paper, we advance tree modeling in the following ways: First, we define a physically-plausible soil model to simulate resource gradients, such as water and nutrients. Second, we propose a novel developmental procedural model for tree roots that enables us to emergently develop root systems that adapt to various soil types. Third, we define long-distance signaling to coordinate the development of shoots and roots. We show that our advanced procedural model of tree development enables - for the first time - the generation of trees with their root systems.

Abstract Procedural modeling allows for an automatic generation of large amounts of similar assets, but there is limited control over the generated output. We address this problem by introducing Automatic Differentiable Procedural Modeling (ADPM). The forward procedural model generates a final editable model. The user modifies the output interactively, and the modifications are transferred back to the procedural model as its parameters by solving an inverse procedural modeling problem. We present an auto‐differentiable representation of the procedural model that significantly accelerates optimization. In ADPM the procedural model is always available, all changes are non‐destructive, and the user can interactively model the 3D object while keeping the procedural representation. ADPM provides the user with precise control over the resulting model comparable to non‐procedural interactive modeling. ADPM is node‐based, and it generates hierarchical 3D scene geometry converted to a differentiable computational graph. Our formulation focuses on the differentiability of high‐level primitives and bounding volumes of components of the procedural model rather than the detailed mesh geometry. Although this high‐level formulation limits the expressiveness of user edits, it allows for efficient derivative computation and enables interactivity. We designed a new optimizer to solve for inverse procedural modeling. It can detect that an edit is under‐determined and has degrees of freedom. Leveraging cheap derivative evaluation, it can explore the region of optimality of edits and suggest various configurations, all of which achieve the requested edit differently. We show our system's efficiency on several examples, and we validate it by a user study.

Abstract This paper proposes and evaluates a sketching language to author crowd motion. It focuses on the path, speed, thickness, and density parameters of crowd motion. A sketch‐based vocabulary is proposed for each parameter and evaluated in a user study against complex crowd scenes. A sketch recognition pipeline converts the sketches into a crowd simulation. The user study results show that 1) participants at various skill levels and can draw accurate crowd motion through sketching, 2) certain sketch styles lead to a more accurate representation of crowd parameters, and 3) sketching allows to produce complex crowd motions in a few seconds. The results show that some styles although accurate actually are less preferred over less accurate ones.

Terrains are visually prominent and commonly needed objects in many computer graphics applications. While there are many algorithms for synthetic terrain generation, it is rather difficult to assess the realism of a generated output. This article presents a first step toward the direction of perceptual evaluation for terrain models. We gathered and categorized several classes of real terrains, and we generated synthetic terrain models using computer graphics methods. The terrain geometries were rendered by using the same texturing, lighting, and camera position. Two studies on these image sets were conducted, ranking the terrains perceptually, and showing that the synthetic terrains are perceived as lacking realism compared to the real ones. We provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, and so forth. We show that the presence or absence of certain features has a significant perceptual effect. The importance and presence of the terrain features were confirmed by using a generative deep neural network that transferred the features between the geometric models of the real terrains and the synthetic ones. The feature transfer was followed by another perceptual experiment that further showed their importance and effect on perceived realism. We then introduce Perceived Terrain Realism Metrics (PTRM), which estimates human-perceived realism of a terrain represented as a digital elevation map by relating the distribution of terrain features with their perceived realism. This metric can be used on a synthetic terrain, and it will output an estimated level of perceived realism. We validated the proposed metrics on real and synthetic data and compared them to the perceptual studies.

The placement of vegetation plays a central role in the realism of virtual scenes. We introduce procedural placement models (PPMs) for vegetation in urban layouts. PPMs are environmentally sensitive to city geometry and allow identifying plausible plant positions based on structural and functional zones in an urban layout. PPMs can either be directly used by defining their parameters or learned from satellite images and land register data. This allows us to populate urban landscapes with complex 3D vegetation and enhance existing approaches for generating urban landscapes. Our framework’s effectiveness is shown through examples of large-scale city scenes and close-ups of individually grown tree models. We validate the results generated with our framework with a perceptual user study and its usability based on urban scene design sessions with expert users.

We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow’s characteristics, such as angle and softness. SSN employs an Ambient Occlusion Prediction module to predict an intermediate ambient occlusion map, which can be further refined by the user to provides geometric cues to modulate the shadow generation. To train our model, we design an efficient pipeline to produce diverse soft shadow training data using 3D object models. In addition, we propose an inverse shadow map representation to improve model training. We demonstrate that our model produces realistic soft shadows in real-time. Our user studies show that the generated shadows are often indistinguishable from shadows calculated by a physics-based renderer and users can easily use SSN through an interactive application to generate specific shadow effects in minutes.

Efficient urban layout generation is an interesting and important problem in many applications dealing with computer graphics and entertainment. We introduce a novel framework for intuitive and controllable small and large-scale urban layout editing. The key inspiration comes from the observation that cities develop in small incremental changes e.g., a building is replaced, or a new road is created. We introduce a set of atomic operations that consistently modify the city. For example, two buildings are merged, a block is split in two, etc. Our second inspiration comes from volumetric editings, such as clay manipulation, where the manipulated material is preserved. The atomic operations are used in interactive brushes that consistently modify the urban layout. The city is populated with agents. Like volume transfer, the brushes attract or repulse the agents, and blocks can be merged and populated with smaller buildings. We also introduce a large-scale brush that repairs a part of the city by learning style as distributions of orientations and intersections.

Abstract We review recent methods in 2D creative pattern generation and their control mechanisms, focusing on procedural methods. The review is motivated by an artist's perspective and investigates interactive pattern generation as a complex design problem. While the repetitive nature of patterns is well‐suited to algorithmic creation and automation, an artist needs more flexible control mechanisms for adaptable and inventive designs. We organize the state of the art around pattern design features, such as repetition, frames, curves, directionality, and single visual accents. Within those areas, we summarize and discuss the techniques' control mechanisms for enabling artist intent. The discussion includes questions of how input is given by the artist, what type of content the artist inputs, where the input affects the canvas spatially, and when input can be given in the timeline of the creation process. We categorize the available control mechanisms on an algorithmic level and categorize their input modes based on exemplars, parameterization, handling, filling, guiding, and placing interactions. To better understand the potential of the current techniques for creative design and to make such an investigation more manageable, we motivate our discussion with how navigation, transparency, variation, and stimulation enable creativity. We conclude our review by identifying possible new directions that can inspire innovation for artist‐centered creation processes and algorithms.

We present a novel method for authoring landscapes with flora and fauna while considering their mutual interactions. Our algorithm outputs a steady-state ecosystem in the form of density maps for each species, their daily circuits, and a modified terrain with eroded trails from a terrain, climatic conditions, and species with related biological information. We introduce the Resource Access Graph, a new data structure that encodes both interactions between food chain levels and animals traveling between resources over the terrain. A novel competition algorithm operating on this data progressively computes a steady-state solution up the food chain, from plants to carnivores. The user can explore the resulting landscape, where plants and animals are instantiated on the fly, and interactively edit it by over-painting the maps. Our results show that our system enables the authoring of consistent landscapes where the impact of wildlife is visible through animated animals, clearings in the vegetation, and eroded trails. We provide quantitative validation with existing ecosystems and a user-study with expert paleontologist end-users, showing that our system enables them to author and compare different ecosystems illustrating climate changes over the same terrain while enabling relevant visual immersion into consistent landscapes.

We introduce a novel method for reconstructing the 3D geometry of botanical trees from single photographs. Faithfully reconstructing a tree from single-view sensor data is a challenging and open problem because many possible 3D trees exist that fit the tree's shape observed from a single view. We address this challenge by defining a reconstruction pipeline based on three neural networks. The networks simultaneously mask out trees in input photographs, identify a tree's species, and obtain its 3D radial bounding volume - our novel 3D representation for botanical trees. Radial bounding volumes (RBV) are used to orchestrate a procedural model primed on learned parameters to grow a tree that matches the main branching structure and the overall shape of the captured tree. While the RBV allows us to faithfully reconstruct the main branching structure, we use the procedural model's morphological constraints to generate realistic branching for the tree crown. This constraints the number of solutions of tree models for a given photograph of a tree. We show that our method reconstructs various tree species even when the trees are captured in front of complex backgrounds. Moreover, although our neural networks have been trained on synthetic data with data augmentation, we show that our pipeline performs well for real tree photographs. We evaluate the reconstructed geometries with several metrics, including leaf area index and maximum radial tree distances.

We present TreePartNet , a neural network aimed at reconstructing tree geometry from point clouds obtained by scanning real trees. Our key idea is to learn a natural neural decomposition exploiting the assumption that a tree comprises locally cylindrical shapes. In particular, reconstruction is a two-step process. First, two networks are used to detect priors from the point clouds. One detects semantic branching points, and the other network is trained to learn a cylindrical representation of the branches. In the second step, we apply a neural merging module to reduce the cylindrical representation to a final set of generalized cylinders combined by branches. We demonstrate results of reconstructing realistic tree geometry for a variety of input models and with varying input point quality, e.g., noise, outliers, and incompleteness. We evaluate our approach extensively by using data from both synthetic and real trees and comparing it with alternative methods.

Many algorithms for virtual tree generation exist, but the visual realism of the 3D models is unknown. This problem is usually addressed by performing limited user studies or by a side-by-side visual comparison. We introduce an automated system for realism assessment of the tree model based on their perception. We conducted a user study in which 4,000 participants compared over one million pairs of images to collect subjective perceptual scores of a large dataset of virtual trees. The scores were used to train two neural-network-based predictors. A view independent ICTreeF uses the tree model's geometric features that are easy to extract from any model. The second is ICTreeI that estimates the perceived visual realism of a tree from its image. Moreover, to provide an insight into the problem, we deduce intrinsic attributes and evaluate which features make trees look like real trees. In particular, we show that branching angles, length of branches, and widths are critical for perceived realism. We also provide three datasets: carefully curated 3D tree geometries and tree skeletons with their perceptual scores, multiple views of the tree geometries with their scores, and a large dataset of images with scores suitable for training deep neural networks.

We introduce QuadStack, a novel algorithm for volumetric data compression and direct rendering. Our algorithm exploits the data redundancy often found in layered datasets which are common in science and engineering fields such as geology, biology, mechanical engineering, medicine, etc. QuadStack first compresses the volumetric data into vertical stacks which are then compressed into a quadtree that identifies and represents the layered structures at the internal nodes. The associated data (color, material, density, etc.) and shape of these layer structures are decoupled and encoded independently, leading to high compression rates (4× to 54× of the original voxel model memory footprint in our experiments). We also introduce an algorithm for value retrieving from the QuadStack representation and we show that the access has logarithmic complexity. Because of the fast access, QuadStack is suitable for efficient data representation and direct rendering. We show that our GPU implementation performs comparably in speed with the state-of-the-art algorithms (18-79 MRays/s in our implementation), while maintaining a significantly smaller memory footprint.

Procedural modeling has produced amazing results, yet fundamental issues such as controllability and limited user guidance persist. We introduce a novel procedural model called PICO (Procedural Iterative Constrained Optimizer) and PICO-Graph that is the underlying procedural model designed with optimization in mind. The key novelty of PICO is that it enables the exploration of generative designs by combining both user and environmental constraints into a single framework by using optimization without the need to write procedural rules. The PICO-Graph procedural model consists of a set of geometry generating operations and a set of axioms connected in a directed cyclic graph. The forward generation is initiated by a set of axioms that use the connections to send coordinate systems and geometric objects through the PICO-Graph, which in turn generates more objects. This allows for fast generation of complex and varied geometries. Moreover, we combine PICO-Graph with efficient optimization that allows for quick exploration of the generated models and the generation of variants. The user defines the rules, the axioms, and the set of constraints; for example, whether an existing object should be supported by the generated model, whether symmetries exist, whether the object should spin, etc. PICO then generates a class of geometric models and optimizes them so that they fulfill the constraints. The generation and the optimization in our implementation provides interactive user control during model execution providing continuous feedback. For example, the user can sketch the constraints and guide the geometry to meet these specified goals. We show PICO on a variety of examples such as the generation of procedural chairs with multiple supports, generation of support structures for 3D printing, generation of spinning objects, or generation of procedural terrains matching a given input. Our framework could be used as a component in a larger design workflow; its strongest application is in the early rapid ideation and prototyping phases.

We introduce a new inverse modeling method to interactively design crowd animations. Few works focus on providing succinct high-level and large-scale crowd motion modeling. Our methodology is to read in real or virtual agent trajectory data and automatically infer a set of parameterized crowd motion models. Then, components of the motion models can be mixed, matched, and altered enabling rapidly producing new crowd motions. Our results show novel animations using real-world data, using synthetic data, and imitating real-world scenarios. Moreover, by combining our method with our interactive crowd trajectory sketching tool, we can create complex spatio-temporal crowd animations in about a minute.

Abstract We introduce a novel semi‐procedural approach that avoids drawbacks of procedural textures and leverages advantages of data‐driven texture synthesis. We split synthesis in two parts: 1) structure synthesis, based on a procedural parametric model and 2) color details synthesis, being data‐driven. The procedural model consists of a generic Point Process Texture Basis Function (PPTBF), which extends sparse convolution noises by defining rich convolution kernels. They consist of a window function multiplied with a correlated statistical mixture of Gabor functions, both designed to encapsulate a large span of common spatial stochastic structures, including cells, cracks, grains, scratches, spots, stains, and waves. Parameters can be prescribed automatically by supplying binary structure exemplars. As for noise‐based Gaussian textures, the PPTBF is used as stand‐alone function, avoiding classification tasks that occur when handling multiple procedural assets. Because the PPTBF is based on a single set of parameters it allows for continuous transitions between different visual structures and an easy control over its visual characteristics. Color is consistently synthesized from the exemplar using a multiscale parallel texture synthesis by numbers, constrained by the PPTBF. The generated textures are parametric, infinite and avoid repetition. The data‐driven part is automatic and guarantees strong visual resemblance with inputs.

We present a framework for multi-objective optimization of fruit tree pruning within a simulated environment, where pruning is performed on a virtual tree model, and its effects on tree growth are observed. The proposed framework uses quantitative measures to express the short-term and long-term effects of pruning, for which potentially conflicting optimization objectives can be defined. The short-term objectives are evaluated on the pruned tree model directly, while the values of long-term objectives are estimated by executing a tree growth simulation. We demonstrate the concept by using a bi-objective case, where the estimated light interceptions of the pruned tree in the current and the next season are used to define separate optimization objectives. We compare the performance of the multi-objective simulated annealing and the NSGA-II method in building the sets of non-dominated pruning solutions. The obtained Pareto front approximations correspond to diverse pruning solutions that balance between optimizing either objective to different extents, which indicates a potential for new applications of the multi-objective pruning optimization concept.

We introduce an inverse procedural modeling approach that learns L-system representations of pixel images with branching structures. Our fully automatic model generates a compact set of textual rewriting rules that describe the input. We use deep learning to discover atomic structures such as line segments or branchings. Orientation and scaling of these structures are determined and the detected structures are combined into a tree. The initial representation is analyzed, and repeating parts are encoded into a small grammar by using greedy optimization while the user can control the size of the detected rules. The output is an L-system that represents the input image as a simple text and a set of terminal symbols. We apply our approach to a variety of examples, demonstrate its robustness against noise and blur, and we show that it can detect user sketches and complex input structures.

We introduce Dendry, a procedural function that generates dendritic patterns and is locally computable. The function is controlled by parameters such as the level of branching, the degree of local smoothing, random seeding and local disturbance parameters, and the range of the branching angles. It is also controlled by a global control function that defines the overall shape and can be used, for example, to initialize local minima. The algorithm returns the distance to a tree structure which is implicitly constructed on the fly, while requiring a small memory footprint. The evaluation can be performed in parallel for multiple points and scales linearly with the number of cores. We demonstrate an application of our model to the generation of terrain heighfields with consistent river networks. A quad core implementation of our algorithm takes about ten seconds for a 512 × 512 resolution grid on the CPU.

Abstract Terrains are a crucial component of three‐dimensional scenes and are present in many Computer Graphics applications. Terrain modeling methods focus on capturing landforms in all their intricate detail, including eroded valleys arising from the interplay of varied phenomena, dendritic mountain ranges, and complex river networks. Set against this visual complexity is the need for user control over terrain features, without which designers are unable to adequately express their artistic intent. This article provides an overview of current terrain modeling and authoring techniques, organized according to three categories: procedural modeling, physically‐based simulation of erosion and land formation processes, and example‐based methods driven by scanned terrain data. We compare and contrast these techniques according to several criteria, specifically: the variety of achievable landforms; realism from both a perceptual and geomorphological perspective; issues of scale in terms of terrain extent and sampling precision; the different interaction metaphors and attendant forms of user‐control, and computation and memory performance. We conclude with an in‐depth discussion of possible research directions and outstanding technical and scientific challenges.

Abstract This paper addresses the problem of creating animated riverscapes through a novel procedural framework that generates the inscribing geometry of a river network and then synthesizes matching real‐time water movement animation. Our approach takes bare‐earth heightfields as input, derives hydrologically‐inspired river network trajectories, carves riverbeds into the terrain, and then automatically generates a corresponding blend‐flow tree for the water surface. Characteristics, such as the riverbed width, depth and shape, as well as elevation and flow of the fluid surface, are procedurally derived from the terrain and river type. The riverbed is inscribed by combining compactly supported elevation modifiers over the river course. Subsequently, the water surface is defined as a time‐varying continuous function encoded as a blend‐flow tree with leaves that are parameterized procedural flow primitives and internal nodes that are blend operators. While river generation is fully automated, we also incorporate intuitive interactive editing of both river trajectories and individual riverbed and flow primitives. The resulting framework enables the generation of a wide range of river forms, ranging from slow meandering rivers to rapids with churning water, including surface effects, such as foam and leaves carried downstream.

Statics is a backbone course for several engineering disciplines and also a pre-requisite for dynamics and mechanics of materials. Researchers have identified a lack of understanding of statics as a significant source of difficulties in terms of both conceptual understanding, representation of free body diagrams (FBD) and problem-solving ability. Our approach to improve the learning of the concept of friction focuses on students' understanding of acting forces and specific components of such forces of a system. The presented quasi-experimental study investigates how the use of visuo-haptic simulations can improve students understanding and use of FBD. Specifically, we compared two visuo-haptic simulations; one that explicitly visually depicts FBD of multiple objects interacting with different surfaces, while the other only provides haptic feedback while students engage in the same forms of interaction. Our results suggest that using the visuo-haptic simulator with FBD leads to better learning results. These findings support the hypothesis that appropriately sequenced visuo-haptic simulators with well-designed visual cues can help students to better understand and use FBD.

Structural analysis is a foundational statics concept for students majoring in mechanical engineering, civil engineering, and engineering technology, among others. However, the mathematical emphasis of a typical statics courses lies in algebraic calculations, matrices, vectors, and sometimes deemphasizes student understanding of the behavior of the overall structure as a system, focusing instead on its individual elements. This study investigates students' conceptual understanding of forces acting and reacting in a truss structure as well as their corresponding representations in the form of Free Body Diagrams (FBDs). Our findings suggest that students primarily demonstrated partially coherent answers suggesting that they may hold some misconceptions about truss behavior. The most prevalent error was that students failed to account for the mutual (equal and opposite) forces between connected bodies that were separated for analysis. Based on our findings we propose the design of a learning experience that combines principles of embodied learning with the affordances of visuohaptic simulations to address students' misconceptions.

Abstract We introduce a novel method for interactive generation of visually consistent, snow‐covered landscapes and provide control of their dynamic evolution over time. Our main contribution is the real‐time phenomenological simulation of avalanches and other user‐guided events, such as tracks left by Nordic skiing, which can be applied to interactively sculpt the landscape. The terrain is modeled as a height field with additional layers for stable, compacted, unstable, and powdery snow, which behave in combination as a semi‐viscous fluid. We incorporate the impact of several phenomena, including sunlight, temperature, prevailing wind direction, and skiing activities. The snow evolution includes snow‐melt and snow‐drift, which affect stability of the snow mass and the probability of avalanches. A user can shape landscapes and their evolution either with a variety of interactive brushes, or by prescribing events along a winter season time‐line. Our optimized GPU‐implementation allows interactive updates of snow type and depth across a large (10 × 10km) terrain, including real‐time avalanches, making this suitable for visual assets in computer games. We evaluate our method through perceptual comparison against exiting methods and real snow‐depth data.

Most mountain ranges are formed by the compression and folding of colliding tectonic plates. Subduction of one plate causes large-scale asymmetry while their layered composition (or stratigraphy) explains the multi-scale folded strata observed on real terrains. We introduce a novel interactive modeling technique to generate visually plausible, large scale terrains that capture these phenomena. Our method draws on both geological knowledge for consistency and on sculpting systems for user interaction. The user is provided hands-on control on the shape and motion of tectonic plates, represented using a new geologically-inspired model for the Earth crust. The model captures their volume preserving and complex folding behaviors under collision, causing mountains to grow. It generates a volumetric uplift map representing the growth rate of subsurface layers. Erosion and uplift movement are jointly simulated to generate the terrain. The stratigraphy allows us to render folded strata on eroded cliffs. We validated the usability of our sculpting interface through a user study, and compare the visual consistency of the earth crust model with geological simulation results and real terrains.

We present Barcode, a global binary descriptor for images captured from a vehicle-mounted camera with two applications: localization and turn classification. Barcode characterizes an image by encoding the distribution of vertical lines into a binary descriptor: in each vertical stripe of an image, if any vertical line exists the corresponding bit is set to 1, otherwise 0. For localization, our approach uses a database of geolocated images, each having its Barcode precomputed during a preprocessing stage. In the run time, we first generate the binary descriptor for each image and then use the descriptor to find the location in the database via Hamming distance metric. For turn classification, we train a deep neural network that uses a set of Barcodes from consecutive images to classify turns (left, right, straight, and stationary). We show that Barcode extraction can be done at 100-1000~Hz, localization at 10~kHz, and turn classification at 1~kHz. We show compelling experimental results on KITTI dataset and other sequences captured near Harvard and Purdue campuses.

We explored the affordances of using visuo-haptic simulations to improve conceptual understanding and representational competence of the concept of friction. Visuohaptic simulations are computer-based simulations that encode mathematical and physical models of certain phenomena and provide visual and tactile feedback. Users can see the simulation and feel the friction with their hand by using a special device connected to a computer. We hypothesized that visual and haptic feedback together can help students to improve learning of friction. We recruited 24 engineering technology students with a previous experience in at least one physics course, and we examined their reasoning and understanding about statics concepts before and after engaging with visuo-haptic simulations. Our instructional approach included four steps: 1) lecture about friction, 2) pretest, 3) laboratory session, and 4) posttest. The laboratory session consisted of a pre-training session, guided learning materials based on a constructivist framework, and use of the friction visuo-haptic simulation. We report students' prior conceptions of statics concepts, ways in which they interacted and reasoned with each of the different pedagogical tools, and compared reasoning processes, explanations and learning gains. Our results suggest that the visuo-haptic simulation helped students refine their explanations and increased the coherence between their verbal explanation and mathematical representation.

Abstract We present a novel approach for style retargeting to non‐humanoid characters by allowing extracted stylistic features from one character to be added to the motion of another character with a different body morphology. We introduce the concept of groups of body parts (GBPs), for example, the torso, legs and tail, and we argue that they can be used to capture the individual style of a character motion. By separating GBPs from a character, the user can define mappings between characters with different morphologies. We automatically extract the motion of each GBP from the source, map it to the target and then use a constrained optimization to adjust all joints in each GBP in the target to preserve the original motion while expressing the style of the source. We show results on characters that present different morphologies to the source motion from which the style is extracted. The style transfer is intuitive and provides a high level of control. For most of the examples in this paper, the definition of GBP takes around 5 min and the optimization about 7 min on average. For the most complicated examples, the definition of three GBPs and their mapping takes about 10 min and the optimization another 30 min.

We present a novel system for the interactive modeling of developmental climbing plants with an emphasis on efficient control and plausible physics response. A plant is represented by a set of connected anisotropic particles that respond to the surrounding environment and to their inner state. Each particle stores biological and physical attributes that drive growth and plant adaptation to the environment such as light sensitivity, wind interaction, and physical obstacles. This representation allows for the efficient modeling of external effects that can be induced at any time without prior analysis of the plant structure. In our framework we exploit this representation to provide powerful editing capabilities that allow to edit a plant with respect to its structure and its environment while maintaining a biologically plausible appearance. Moreover, we couple plants with Lagrangian fluid dynamics and model advanced effects, such as the breaking and bending of branches. The user can thus interactively drag and prune branches or seed new plants in dynamically changing environments. Our system runs in real-time and supports up to 20 plant instances with 25k branches in parallel. The effectiveness of our approach is demonstrated through a number of interactive experiments, including modeling and animation of different species of climbing plants on complex support structures.

We introduce a novel framework for interactive landscape authoring that supports bi-directional feedback between erosion and vegetation simulation. Vegetation and terrain erosion have strong mutual impact and their interplay influences the overall realism of virtual scenes. Despite their importance, these complex interactions have been neglected in computer graphics. Our framework overcomes this by simulating the effect of a variety of geomorphological agents and the mutual interaction between different material and vegetation layers, including rock, sand, humus, grass, shrubs, and trees. Users are able to exploit these interactions with an authoring interface that consistently shapes the terrain and populates it with details. Our method, validated through side-by-side comparison with real terrains, can be used not only to generate realistic static landscapes, but also to follow the temporal evolution of a landscape over a few centuries.

Authoring virtual terrains presents a challenge and there is a strong need for authoring tools able to create realistic terrains with simple user-inputs and with high user control. We propose an example-based authoring pipeline that uses a set of terrain synthesizers dedicated to specific tasks. Each terrain synthesizer is a Conditional Generative Adversarial Network trained by using real-world terrains and their sketched counterparts. The training sets are built automatically with a view that the terrain synthesizers learn the generation from features that are easy to sketch. During the authoring process, the artist first creates a rough sketch of the main terrain features, such as rivers, valleys and ridges, and the algorithm automatically synthesizes a terrain corresponding to the sketch using the learned features of the training samples. Moreover, an erosion synthesizer can also generate terrain evolution by erosion at a very low computational cost. Our framework allows for an easy terrain authoring and provides a high level of realism for a minimum sketch cost. We show various examples of terrain synthesis created by experienced as well as inexperienced users who are able to design a vast variety of complex terrains in a very short time.

We introduce Skippy, a novel algorithm for 3D interactive curve modeling from a single view. While positing curves in space can be a tedious task, our rapid sketching algorithm allows users to draw curves in and around existing geometry in a controllable manner. The key insight behind our system is to automatically infer the 3D curve coordinates by enumerating a large set of potential curve trajectories. More specifically, we partition 2D strokes into continuous segments that land both on and off the geometry, duplicating segments that could be placed in front or behind, to form a directed graph. We use distance fields to estimate 3D coordinates for our curve segments and solve for an optimally smooth path that follows the curvature of the scene geometry while avoiding intersections. Using our curve design framework we present a collection of novel editing operations allowing artists to rapidly explore and refine the combinatorial space of solutions. Furthermore, we include the quick placement of transient geometry to aid in guiding the 3D curve. Finally we demonstrate our interactive design curve system on a variety of applications including geometric modeling, and camera motion path planning.

Interactions play a key role in understanding objects and scenes for both virtual and real-world agents. We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved. The representation is based on tracking particles on one of the participating objects and then observing them with sensors appropriately placed in the interaction volume or on the interaction surfaces. We show how to factorize these interaction descriptors and project them into a particular participating object so as to obtain a new functional descriptor for that object, its interaction landscape , capturing its observed use in a spatiotemporal framework. Interaction landscapes are independent of the particular interaction and capture subtle dynamic effects in how objects move and behave when in functional use. Our method relates objects based on their function, establishes correspondences between shapes based on functional key points and regions, and retrieves peer and partner objects with respect to an interaction.

Surface remeshing is a key component in many geometry processing applications. The typical goal consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application (e.g., the minimum interior angle is above an application-dependent threshold). Our algorithm is designed to address all three optimization goals simultaneously by targeting prescribed bounds on approximation error , minimal interior angle and maximum mesh complexity (number of vertices). The approximation error bound is a hard constraint, while the other two criteria are modeled as optimization goals to guarantee feasibility. Our optimization framework applies carefully prioritized local operators in order to greedily search for the coarsest mesh with minimal interior angle above and approximation error bounded by . Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that for reasonable angle bounds ( ) our approach delivers high-quality meshes with implicitly preserved features (no tagging required) and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.

Inverse procedural modeling discovers a procedural representation of an existing geometric model and the discovered procedural model then supports synthesizing new similar models. We introduce an automatic approach that generates a compact, efficient, and re-usable procedural representation of a polygonal 3D architectural model. This representation is then used for structure-aware editing and synthesis of new geometric models that resemble the original. Our framework captures the pattern hierarchy of the input model into a split tree data representation. A context-free split grammar, supporting a hierarchical nesting of procedural rules, is extracted from the tree, which establishes the base of our interactive procedural editing engine. We show the application of our approach to a variety of architectural structures obtained by procedurally editing web-sourced models. The grammar generation takes a few minutes even for the most complex input and synthesis is fully interactive for buildings composed of up to 200k polygons.

Abstract At large scale, landscapes result from the combination of two major processes: tectonics which generate the main relief through crust uplift, and weather which accounts for erosion. This paper presents the first method in computer graphics that combines uplift and hydraulic erosion to generate visually plausible terrains. Given a user‐painted uplift map, we generate a stream graph over the entire domain embedding elevation information and stream flow. Our approach relies on the stream power equation introduced in geology for hydraulic erosion. By combining crust uplift and stream power erosion we generate large realistic terrains at a low computational cost. Finally, we convert this graph into a digital elevation model by blending landform feature kernels whose parameters are derived from the information in the graph. Our method gives high‐level control over the large scale dendritic structures of the resulting river networks, watersheds, and mountains ridges.

Abstract We propose a novel approach for authoring large scenes with automatic enhancement of objects to create geometric decoration details such as snow cover, icicles, fallen leaves, grass tufts or even trash. We introduce environmental objects that extend an input object geometry with a set of procedural effects that defines how the object reacts to the environment, and by a set of scalar fields that defines the influence of the object over of the environment. The user controls the scene by modifying environmental variables, such as temperature or humidity fields. The scene definition is hierarchical: objects can be grouped and their behaviours can be set at each level of the hierarchy. Our per object definition allows us to optimize and accelerate the effects computation, which also enables us to generate large scenes with many geometric details at a very high level of detail. In our implementation, a complex urban scene of 10 000 m2, represented with details of less than 1 cm, can be locally modified and entirely regenerated in a few seconds.

3D modeling remains a notoriously difficult task for novices despite significant research effort to provide intuitive and automated systems. We tackle this problem by combining the strengths of two popular domains: sketch-based modeling and procedural modeling. On the one hand, sketch-based modeling exploits our ability to draw but requires detailed, unambiguous drawings to achieve complex models. On the other hand, procedural modeling automates the creation of precise and detailed geometry but requires the tedious definition and parameterization of procedural models. Our system uses a collection of simple procedural grammars, called snippets, as building blocks to turn sketches into realistic 3D models. We use a machine learning approach to solve the inverse problem of finding the procedural model that best explains a user sketch. We use non-photorealistic rendering to generate artificial data for training convolutional neural networks capable of quickly recognizing the procedural rule intended by a sketch and estimating its parameters. We integrate our algorithm in a coarse-to-fine urban modeling system that allows users to create rich buildings by successively sketching the building mass, roof, facades, windows, and ornaments. A user study shows that by using our approach non-expert users can generate complex buildings in just a few minutes.

We develop a new kind of "space-filling" curves, connected Fermat spirals , and show their compelling properties as a tool path fill pattern for layered fabrication. Unlike classical space-filling curves such as the Peano or Hilbert curves, which constantly wind and bind to preserve locality, connected Fermat spirals are formed mostly by long, low-curvature paths. This geometric property, along with continuity, influences the quality and efficiency of layered fabrication. Given a connected 2D region, we first decompose it into a set of sub-regions, each of which can be filled with a single continuous Fermat spiral. We show that it is always possible to start and end a Fermat spiral fill at approximately the same location on the outer boundary of the filled region. This special property allows the Fermat spiral fills to be joined systematically along a graph traversal of the decomposed sub-regions. The result is a globally continuous curve. We demonstrate that printing 2D layers following tool paths as connected Fermat spirals leads to efficient and quality fabrication, compared to conventional fill patterns.

Haptic devices are electro-mechanical tools controlled by computers that allow to recreate the sense of touch. They enhance the sense of interaction with virtual objects from purely visual to haptic and visual. One of its application areas is in training environments, where the users can interact with virtual objects to learn procedures or tasks. In this paper we describe the use of haptic devices to improve the learning process of basic physics concepts from electromagnetism and the haptic tools via simulation of magnetic forces in 3D. We have created three scenarios with different distribution of charges: point charge, line charge, and plane charge. Each scenario was properly calibrated and has different force feedback (quadratic, linear, and constant) depending on the scenario. We wanted to investigate how forces are perceived by students. A user study was carried out to assess students' perception and knowledge acquired when they were working with the system. Results suggest that students from the treatment group achieved better understanding than those from the control group. Results also indicate that 95% of the students considered that the use of haptic devices combined with appropriate virtual environments facilitated them to understand the nature and origin of electrical forces.

Thanks to the recent advances in computational photography and remote sensing, point clouds of buildings are becoming increasingly available, yet their processing poses various challenges. In our work, we tackle the problem of point cloud completion and editing and we approach it via inverse procedural modeling. Contrary to the previous work, our approach operates directly on the point cloud without an intermediate triangulation. Our approach consists of 1) semi-automatic segmentation of the input point cloud with segment comparison and template matching to detect repeating structures, 2) a consensus-based voting schema and a pattern extraction algorithm to discover completed terminal geometry and their patterns of usage, all encoded into a context-free grammar, and 3) an interactive editing tool where the user can create new point clouds by using procedural copy and paste operations, and smart resizing. We demonstrate our approach on editing of building models with up to 1.8M points. In our implementation, preprocessing takes up to several minutes and a single editing operation needs from one second to one minute depending on the model size and the operation type.

Abstract We introduce a compact hierarchical procedural model that combines feature‐based primitives to describe complex terrains with varying level of detail. Our model is inspired by skeletal implicit surfaces and defines the terrain elevation function by using a construction tree. Leaves represent terrain features and they are generic parametrized skeletal primitives, such as mountains, ridges, valleys, rivers, lakes or roads. Inner nodes combine the leaves and subtrees by carving, blending or warping operators. The elevation of the terrain at a given point is evaluated by traversing the tree and by combining the contributions of the primitives. The definition of the tree leaves and operators guarantees that the resulting elevation function is Lipschitz, which speeds up the sphere tracing used to render the terrain. Our model is compact and allows for the creation of large terrains with a high level o detail using a reduced set of primitives. We show the creation of different kinds of landscapes and demonstrate that our model allows to efficiently control the shape and distribution of landform features.

Abstract We present a botanical simulation of secondary (cambial) tree growth coupled to a physical cracking simulation of its bark. Whereas level set growth would use a fixed resolution voxel grid, our system extends the deformable simplicial complex (DSC), supporting new biological growth functions robustly on any surface polygonal mesh with adaptive subdivision, collision detection and topological control. We extend the DSC with temporally coherent texturing, and surface cracking with a user‐controllable biological model coupled to the stresses introduced by the cambial growth model.

We pose the decompose-and-pack or DAP problem, which tightly combines shape decomposition and packing. While in general, DAP seeks to decompose an input shape into a small number of parts which can be efficiently packed, our focus is geared towards 3D printing. The goal is to optimally decompose-and-pack a 3D object into a printing volume to minimize support material, build time, and assembly cost. We present Dapper , a global optimization algorithm for the DAP problem which can be applied to both powder- and FDM-based 3D printing. The solution search is top-down and iterative. Starting with a coarse decomposition of the input shape into few initial parts, we progressively pack a pile in the printing volume, by iteratively docking parts, possibly while introducing cuts, onto the pile. Exploration of the search space is via a prioritized and bounded beam search , with breadth and depth pruning guided by local and global DAP objectives. A key feature of Dapper is that it works with pyramidal primitives, which are packing- and printing-friendly. Pyramidal shapes are also more general than boxes to reduce part counts, while still maintaining a suitable level of simplicity to facilitate DAP optimization. We demonstrate printing efficiency gains achieved by Dapper, compare to state-of-the-art alternatives, and show how fabrication criteria such as cut area and part size can be easily incorporated into our solution framework to produce more physically plausible fabrications.

Recent shape retrieval and interactive modeling algorithms enable the re-use of existing models in many applications. However, most of those techniques require a pre-labeled model with some semantic information. We introduce a fully automatic approach to simultaneously segment and detect similarities within an existing 3D architectural model. Our framework approaches the segmentation problem as a weighted minimum set cover over an input triangle soup, and maximizes the repetition of similar segments to find a best set of unique component types and instances. The solution for this set-cover formulation starts with a search space reduction to eliminate unlikely combinations of triangles, and continues with a combinatorial optimization within each disjoint subspace that outputs the components and their types. We show the discovered components of a variety of architectural models obtained from public databases. We demonstrate experiments testing the robustness of our algorithm, in terms of threshold sensitivity, vertex displacement, and triangulation variations of the original model. In addition, we compare our components with those of competing approaches and evaluate our results against user-based segmentations. We have processed a database of 50 buildings, with various structures and over 200K polygons per building, with a segmentation time averaging up to 4 minutes.

We present a novel approach for the interactive synthesis and editing of virtual worlds. Our method is inspired by painting operations and uses methods for statistical example-based synthesis to automate content synthesis and deformation. Our real-time approach takes a form of local inverse procedural modeling based on intermediate statistical models: selected regions of procedurally and manually constructed example scenes are analyzed, and their parameters are stored as distributions in a palette, similar to colors on a painter's palette. These distributions can then be interactively applied with brushes and combined in various ways, like in painting systems. Selected regions can also be moved or stretched while maintaining the consistency of their content. Our method captures both distributions of elements and structured objects, and models their interactions. Results range from the interactive editing of 2D artwork maps to the design of 3D virtual worlds, where constraints set by the terrain's slope are also taken into account.

We present a framework for the conversion of existing 3D unstructured urban models into a compact procedural representation that enables model synthesis, querying, and simplification of large urban areas. During the de-instancing phase, a dissimilarity-based clustering is performed to obtain a set of building components and component types. During the proceduralization phase, the components are arranged into a context-free grammar, which can be directly edited or interactively manipulated. We applied our approach to convert several large city models, with up to 19,000 building components spanning over 180 km squares, into procedural models of a few thousand terminals, non-terminals, and 50-100 rules.

Abstract Procedural modelling deals with (semi‐)automatic content generation by means of a program or procedure. Among other advantages, its data compression and the potential to generate a large variety of detailed content with reduced human intervention, have made procedural modelling attractive for creating virtual environments increasingly used in movies, games and simulations. We survey procedural methods that are useful to generate features of virtual worlds, including terrains, vegetation, rivers, roads, buildings and entire cities. In this survey, we focus particularly on the degree of intuitive control and of interactivity offered by each procedural method, because these properties are instrumental for their typical users: designers and artists. We identify the most promising research results that have been recently achieved, but we also realize that there is far from widespread acceptance of procedural methods among non‐technical, creative professionals. We conclude by discussing some of the most important challenges of procedural modelling.

Abstract Procedural tree models have been popular in computer graphics for their ability to generate a variety of output trees from a set of input parameters and to simulate plant interaction with the environment for a realistic placement of trees in virtual scenes. However, defining such models and their parameters is a difficult task. We propose an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input. Our framework is based on a novel parametric model for tree generation and uses Monte Carlo Markov Chains to find the optimal set of parameters. We demonstrate our approach on a variety of input models obtained from different sources, such as interactive modelling systems, reconstructed scans of real trees and developmental models.

Abstract We introduce an optimization framework for the reduction of support structures required by 3D printers based on Fused Deposition Modeling (FDM) technology. The printers need to connect overhangs with the lower parts of the object or the ground in order to print them. Since the support material needs to be printed first and discarded later, optimizing its volume can lead to material and printing time savings. We present a novel, geometry‐based approach that minimizes the support material while providing sufficient support. Using our approach, the input 3D model is first oriented into a position with minimal area that requires support. Then the points in this area that require support are detected. For these points the supporting structure is progressively built while attempting to minimize the overall length of the support structure. The resulting structure has a tree‐like shape that effectively supports the overhangs. We have tested our algorithm on the MakerBot® Replicator™ 2 printer and we compared our solution to the embedded software solution in this printer and to Autodesk® Meshmixer™ software. Our solution reduced printing time by an average of 29.4% (ranging from 13.9% to 49.5%) and the amount of material by 40.5% (ranging from 24.5% to 68.1%).

Abstract We propose an optimization framework for 3D printing that seeks to save printing time and the support material required to print 3D shapes. Three‐dimensional printing technology is rapidly maturing and may revolutionize how we manufacture objects. The total cost of printing, however, is governed by numerous factors which include not only the price of the printer but also the amount of material and time to fabricate the shape. Our PackMerger framework converts the input 3D watertight mesh into a shell by hollowing its inner parts. The shell is then divided into segments. The location of splits is controlled based on several parameters, including the size of the connection areas or volume of each segment. The pieces are then tightly packed using optimization. The optimization attempts to minimize the amount of support material and the bounding box volume of the packed segments while keeping the number of segments minimal. The final packed configuration can be printed with substantial time and material savings, while also allowing printing of objects that would not fit into the printer volume. We have tested our system on three different printers and it shows a reduction of 5–30% of the printing time while simultaneously saving 15–65% of the support material. The optimization time was approximately 1 min. Once the segments are printed, they need to be assembled.

ABSTRACT A novel hybrid level‐of‐detail (LOD) algorithm is introduced. We combine point‐based, line‐based, and splat‐based rendering to synthesize large‐scale urban city images. We first extract lines and points from the input and provide their simplification encoded in a data structure that allows for a quick and automatic LOD selection. A screen‐space projected area is used as the LOD selector. The algorithm selects lines for long‐distance views providing high contrast and fidelity of the building silhouettes. For medium‐distance views, points are added, and splats are used for close‐up views. Our implementation shows a 10 × speedup as compared with the ground truth models and is about four times faster than geometric LOD. The quality of the results is indistinguishable from the original as confirmed by a user study and two algorithmic metrics. Copyright © 2014 John Wiley & Sons, Ltd.

We present a novel method for combining developmental tree models with turbulent wind fields. The tree geometry is created from internal growth functions of the developmental model and its response to external stress is induced by a physically-plausible wind field that is simulated by Smoothed Particle Hydrodynamics (SPH). Our tree models are dynamically evolving complex systems that (1) react in real-time to high-frequent changes of the wind simulation; and (2) adapt to long-term wind stress. We extend this process by wind-related effects such as branch breaking as well as bud abrasion and drying. In our interactive system the user can adjust the parameters of the growth model, modify wind properties and resulting forces, and define the tree's long-term response to wind. By using graphics hardware, our implementation runs at interactive rates for moderately large scenes composed of up to 20 tree models.

The NEXRAD Level II super resolution Doppler radars continuously scan the atmosphere above the continental USA, providing a stream of temporally and spatially misaligned large volumetric data about cloud reflectivity, wind velocity, and spectrum width. This data is used for immediate and long term weather predictions. However, because this large amount of sparse streaming data is not temporally aligned, the existing approaches rely either on a 2D projection of the 3D data, or the display of the 3D data only for a single radar. We present a framework that enables users to interactively access, analyze, and visualize the Doppler reflectivity data directly in 3D for multiple radars. Our approach extends the existing body of work on large-scale storage of global weather data and out-of-core volume rendering using CUDA ray-casting. The asynchronously streamed reflectivity data from multiple radars are first temporally aligned and then processed to a hierarchical format that is suitable for a large-scale volumetric visualization in near-real time with a minimal run-time processing. This approach also allows for varying precision and level of detail.

Recent years have seen increasing research in perceptually-driven reductions in the costs of realistically rendered imagery. Water is complex and recognizable, and continues to be in the forefront of research. However, the contribution of individual lighting phenomena to the perceived realism of virtual water has not been addressed. All these phenomena have costs associated with their rendering, but does the visual benefit outweigh these costs? This study investigates the human perception of various illumination components found in water-rich virtual environments. The investigation uses a traditional psychophysical analysis to examine viewer perception of these lighting phenomena as they relate to the rendering cost, and ultimately reveals common trends in perceptual value. Five different scenes with a wide range of water and lighting dynamics were tested for perceptual value by one hundred participants. Our results provide an importance comparison for lighting phenomena in the rendering of water, and cost reductions can be made with little or no effect on the perceived quality of the imagery if viewed in a scenario similar to our testing.

We present a framework that allows quick and intuitive modeling of terrains using concepts inspired by hydrology. The terrain is generated from a simple initial sketch, and its generation is controlled by a few parameters. Our terrain representation is both analytic and continuous and can be rendered by using varying levels of detail. The terrain data are stored in a novel data structure: a construction tree whose internal nodes define a combination of operations, and whose leaves represent terrain features. The framework uses rivers as modeling elements, and it first creates a hierarchical drainage network that is represented as a geometric graph over a given input domain. The network is then analyzed to construct watersheds and to characterize the different types and trajectories of rivers. The terrain is finally generated by combining procedural terrain and river patches with blending and carving operators.

We present a dynamic tree modeling and representation technique that allows complex tree models to interact with their environment. Our method uses changes in the light distribution and proximity to solid obstacles and other trees as approximations of biologically motivated transformations on a skeletal representation of the tree's main branches and its procedurally generated foliage. Parts of the tree are transformed only when required, thus our approach is much faster than common algorithms such as Open L-Systems or space colonization methods. Input is a skeleton-based tree geometry that can be computed from common tree production systems or from reconstructed laser scanning models. Our approach enables content creators to directly interact with trees and to create visually convincing ecosystems interactively. We present different interaction types and evaluate our method by comparing our transformations to biologically based growth simulation techniques.

The use of 3D printing has rapidly expanded in the past couple of years. It is now possible to produce 3D-printed objects with exceptionally high fidelity and precision. However, although the quality of 3D printing has improved, both the time to print and the material costs have remained high. Moreover, there is no guarantee that a printed model is structurally sound. The printed product often does not survive cleaning, transportation, or handling, or it may even collapse under its own weight. We present a system that addresses this issue by providing automatic detection and correction of the problematic cases. The structural problems are detected by combining a lightweight structural analysis solver with 3D medial axis approximations. After areas with high structural stress are found, the model is corrected by combining three approaches: hollowing, thickening, and strut insertion. Both detection and correction steps are repeated until the problems have been eliminated. Our process is designed to create a model that is visually similar to the original model but possessing greater structural integrity.

We propose a framework that enables adding intuitive high level control to an existing urban procedural model. In particular, we provide a mechanism to interactively edit urban models, a task which is important to stakeholders in gaming, urban planning, mapping, and navigation services. Procedural modeling allows a quick creation of large complex 3D models, but controlling the output is a well-known open problem. Thus, while forward procedural modeling has thrived, in this paper we add to the arsenal an inverse modeling tool. Users, unaware of the rules of the underlying urban procedural model, can alternatively specify arbitrary target indicators to control the modeling process. The system itself will discover how to alter the parameters of the urban procedural model so as to produce the desired 3D output. We label this process inverse design.

We propose a novel approach for the reconstruction of urban structures from 3D point clouds with an assumption of Manhattan World (MW) building geometry; i.e., the predominance of three mutually orthogonal directions in the scene. Our approach works in two steps. First, the input points are classified according to the MW assumption into four local shape types: walls, edges, corners, and edge corners. The classified points are organized into a connected set of clusters from which a volume description is extracted. The MW assumption allows us to robustly identify the fundamental shape types, describe the volumes within the bounding box, and reconstruct visible and occluded parts of the sampled structure. We show results of our reconstruction that has been applied to several synthetic and real-world 3D point data sets of various densities and from multiple viewpoints. Our method automatically reconstructs 3D building models from up to 10 million points in 10 to 60 seconds.

We address the open problem of spatial distribution of vegetation in urban environments by introducing a user-guided simulation and procedural system for integrating plants into the interactive design process of 3D urban models. Our approach uses as input 3D geometry of an urban layout from which it infers initial conditions and parameters of procedural rules. A level of manageability is calculated for each area of the urban space. The manageability level defines the amount of influence between the wild ecosystem simulation, where the plants compete for resources and seed freely, and the managed ecosystem, where nearly no seeding is allowed and the plants grow only under well-defined conditions. The wild ecosystems are handled by a simulation of plant competition for resources, whereas the procedural generation is based on an expandable set of behavioral rules of owners and typical plant management. Our system provides an interactive semi-automatic method to calculate a spatial plant distribution and to create an urban model with plants covering an area of several square kilometers in less than a minute. It provides a high degree of controllability and works tightly with an urban simulation system. We show various examples, such as plant development over time in managed and unmanaged areas, effect of procedural rules on the plant distribution, and the effect of changing the level of manageability and the plant distribution.

Abstract Procedural methods present one of the most powerful techniques for authoring a vast variety of computer graphics models. However, their massive applicability is hindered by the lack of control and a low predictability of the results. In the classical procedural modeling pipeline, the user usually defines a set of rules, executes the procedural system, and by examining the results attempts to infer what should be changed in the system definition in order to achieve the desired output. We present guided procedural modeling, a new approach that allows a high level of top‐down control by breaking the system into smaller building blocks that communicate. In our work we generalize the concept of the environment. The user creates a set of guides. Each guide defines a region in which a specific procedural model operates. These guides are connected by a set of links that serve for message passing between the procedural models attached to each guide. The entire model consists of a set of guides with procedural models, a graph representing their connection, and the method in which the guides interact. The modeling process is performed by modifying each of the described elements. The user can control the high‐level description by editing the guides or manipulate the low‐level description by changing the procedural rules. Changing the connectivity allows the user to create new complex forms in an easy and intuitive way. We show several examples of procedural structures, including an ornamental pattern, a street layout, a bridge, and a model of trees. We also demonstrate interactive examples for quick and intuitive editing using physics‐based mass‐spring system.

Abstract We present a procedural method for generating hierarchical road networks connecting cities, towns and villages over large terrains. Our approach relies on an original geometric graph generation algorithm based on a non‐Euclidean metric combined with a path merging algorithm that creates junctions between the different types of roads. Unlike previous work, our method allows high level user control by manipulating the density and the pattern of the network. The geometry of the highways, primary and secondary roads as well as the interchanges and intersections are automatically created from the graph structure by instantiating generic parameterized models.

We present a passive computer vision method that exploits existing mapping and navigation databases in order to automatically create 3D building models. Our method defines a grammar for representing changes in building geometry that approximately follow the Manhattan-world assumption which states there is a predominance of three mutually orthogonal directions in the scene. By using multiple calibrated aerial images, we extend previous Manhattan-world methods to robustly produce a single, coherent, complete geometric model of a building with partial textures. Our method uses an optimization to discover a 3D building geometry that produces the same set of façade orientation changes observed in the captured images. We have applied our method to several real-world buildings and have analyzed our approach using synthetic buildings.

Abstract We present an important step towards the solution of the problem of inverse procedural modeling by generating parametric context‐free L‐systems that represent an input 2D model. The L‐system rules efficiently code the regular structures and the parameters represent the properties of the structure transformations. The algorithm takes as input a 2D vector image that is composed of atomic elements, such as curves and poly‐lines. Similar elements are recognized and assigned terminal symbols of an L‐system alphabet. The terminal symbols' position and orientation are pair‐wise compared and the transformations are stored as points in multiple 4D transformation spaces. By careful analysis of the clusters in the transformation spaces, we detect sequences of elements and code them as L‐system rules. The coded elements are then removed from the clusters, the clusters are updated, and then the analysis attempts to code groups of elements in (hierarchies) the same way. The analysis ends with a single group of elements that is coded as an L‐system axiom. We recognize and code branching sequences of linearly translated, scaled, and rotated elements and their hierarchies. The L‐system not only represents the input image, but it can also be used for various editing operations. By changing the L‐system parameters, the image can be randomized, symmetrized, and groups of elements and regular structures can be edited. By changing the terminal and non‐terminal symbols, elements or groups of elements can be replaced.

Abstract This paper presents a new technique for modification of 3D terrains by hydraulic erosion. It efficiently couples fluid simulation using a Lagrangian approach, namely the Smoothed Particle Hydrodynamics (SPH) method, and a physically‐based erosion model adopted from an Eulerian approach. The eroded sediment is associated with the SPH particles and is advected both implicitly, due to the particle motion, and explicitly, through an additional velocity field, which accounts for the sediment transfer between the particles. We propose a new donor‐acceptor scheme for the explicit advection in SPH. Boundary particles associated to the terrain are used to mediate sediment exchange between the SPH particles and the terrain itself. Our results show that this particle‐based method is efficient for the erosion of dense, large, and sparse fluid. Our implementation provides interactive results for scenes with up to 25,000 particles.

The main contribution of our work is in closing the loop between behavioral and geometrical modeling of cities. Editing of urban design variables is performed intuitively and visually using a graphical user interface. Any design variable can be constrained or changed. The design process uses an iterative dynamical system for reaching equilibrium: a state where the demands of behavioral modeling match those of geometrical modeling. 3D models are generated in a few seconds and conform to plausible urban behavior and urban geometry. Our framework includes an interactive agent-based behavioral modeling system as well as adaptive geometry generation algorithms. We demonstrate interactive and incremental design and editing for synthetic urban spaces spanning over 200 square kilometers.

Urban simulation models and their visualization are used to help regional planning agencies evaluate alternative transportation investments, land use regulations, and environmental protection policies. Typical urban simulations provide spatially distributed data about number of inhabitants, land prices, traffic, and other variables. In this article, we build on a synergy of urban simulation, urban visualization, and computer graphics to automatically infer an urban layout for any time step of the simulation sequence. In addition to standard visualization tools, our method gathers data of the original street network, parcels, and aerial imagery and uses the available simulation results to infer changes to the original urban layout and produce a new and plausible layout for the simulation results. In contrast with previous work, our approach automatically updates the layout based on changes in the simulation data and thus can scale to a large simulation over many years. The method in this article offers a substantial step forward in building integrated visualization and behavioral simulation systems for use in community visioning, planning, and policy analysis. We demonstrate our method on several real cases using a 200 GB database for a 16,300 km2 area surrounding Seattle.

We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels.

Abstract This paper presents a generalized solution to modelling hydraulic erosion using ideas from fluid mechanics. The model is based on the Navier–Stokes equations, which provide the dynamics of velocity and pressure. These equations form the basis for the model to balance erosion and deposition that determine changes in the layers between water and erosion material. The eroded material is captured and relocated by water according to a material transport equation. The resulting model is fully 3D and is able to simulate a variety of phenomena including river meanders, low hill sediment wash, natural water springs and receding waterfalls. The simulations show the terrain morphogenesis and can be used for animations as well as for static scene generation. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract The classical work of bird‐like objects of Reynolds simulates polarized motion of groups of oriented particles, bird‐like objects, or simply boids. To do this, three steering vectors are introduced. Cohesion is the tendency of boids to stay in the center of the flock, alignment smoothes their velocities to similar values, and separation helps them to avoid mutual collisions. If no impetus is introduced the boids wander somewhat randomly so an external leading force is necessary for the correct flock behavior. As can be observed during the bird flocking in the fall, birds sometimes move in a way that is not captured by the above described framework. Some of the birds, typically the ones on the edge of the flock, suddenly shoot‐off. The flock then pursues this leader. In the original work by Reynolds the cohesion and separation are two complementary steers. We introduce a complementary force to the alignment that we call the change of leadership. This steer defines the chance of the boid to become a leader and try to escape. The leadership is derived from the boid position and the flock eccentricity. If a boid is on the front edge of the flock it has a higher chance to escape. Escaping from the flock is simulated as a sequence of velocity increases that are added to the current velocity of the boid. The entire system is easy to implement, is efficient, and runs simulations of hundreds of boids on a standard computer at 30 frames per second. Our system is aimed to real‐time simulations and has the potential to be used in games, crowd simulations, etc. Copyright © 2006 John Wiley & Sons, Ltd.

In mainstream geometric modeling, cultivating virtual plant ecosystems is a difficult task. Algorithms for realistic scene generation are rooted in procedural models with no explicit or poor external control. We propose that virtual ecosystems modeling may be boosted using software agents as behavioral tools. An ecosystem grows and is driven by its internal rules of development. If it is left to its own fate, it will reach stability on the edge of chaos. Agents interact with ecosystems by adding plants, cutting or killing them, watering, stepping-over, or favoring some plant species. An agent is a characterization artifact that shows proactive conduct and is described by its set of sensors, effectors, internal states, and habits. Habits are defined as continuous functions and allow for characterizing a wide variety of behaviors.

An old algorithm for visual simulation of climbing plants is extended here. Plants are modeled as systems of oriented particles that are able to sense their environment. Particles move to the best locations using directed random walk. We use the phenomenon of traumatic reiteration for critical cases. If there is no location for further growth possible the particle dies, but before that it sends a signal that is propagated down in the plant structure. This signal activates the closest possible sleeping particle that takes its job. We use an associated voxel space for collisions and space occupancy detection as well as for evaluating the illumination of the plant organs. The algorithm is fast, easy to implement, and runs interactively even for quite large scenes on a medium-class computer. We believe that this approach can be used as an interactive technique in architecture, computer games, computer animation, etc.

Abstract Particle systems have been used in computer graphics for many different purposes, including visual simulation of fur, grass, hair, and similar fuzzy textures and shapes. The underlying theories used in these algorithms are usually quite complex and are mostly based on simulation of diffuse‐limited aggregation, cellular development, reaction‐diffusion models, etc. This leads to high time complexity of these algorithms. The purpose of this paper is to show that collision detection and distance keeping among moving particles can generate similar realistic textures efficiently. This approach is easy to implement, sufficiently fast allowing for interactive modeling, and inherits the major features from the previously published techniques. We first construct a scene consisting of generators of particles, attractors, and cutters. The generators generate oriented particles, and the attractors attract or repulse them. When collision with the cutter is detected, the particle performs an action according to its state and position in the 3D space. Every particle has assigned a table of possible actions that is used for solving these critical states. Trajectories of the particles are then used as a resulting shape of the texture. Copyright © 2001 John Wiley & Sons, Ltd.

This paper presents an algorithm for approximating the sky brightness of a clear sky with sunshine. The sky light and the light reflected from the ground are approximated by a finite set of parallel light beams. The long-term simulation is considered by averaging the radiance of the lights over a certain time period. The main area of interest of the method is considered to be, but is not restricted to, the visual simulation of (botanical) plant development.