Building Information Modeling (BIM) has transformed the Architecture, Engineering, and Construction (AEC) industry by digitizing project data, yet its full potential remains unrealized due to persistent gaps between virtual models and physical sites. These gaps contribute to inefficiencies, with studies reporting substantial waste in labor and coordination. Extended Reality (XR) technologies offer a promising solution by enabling immersive, real-scale visualization of BIM models on-site. This article introduces a Mixed Reality (MR) application for Microsoft HoloLens 2 that superimposes BIM representations onto construction environments at a 1:1 scale, supporting real-time detection of differences between the as-designed BIM model and the as-built construction on site. We present a robust registration pipeline that integrates commercial XR hardware with advanced algorithms to achieve precise alignment under challenging conditions. To validate the system, we conducted a controlled user study comparing three registration paradigms (manual gesture-based, QR-assisted, and fully automatic) and analyzing their impact on alignment accuracy and user experience (UX) in AEC-related tasks. Results show that our automatic approach provides advantages over state-of-the-art alternatives and significantly improves registration precision and usability ratings over the baseline methods. Furthermore, the study demonstrates that alignment errors strongly influence spatial perception and decision-making, highlighting the necessity of high-fidelity registration for effective MR integration in construction workflows.

Abstract Human motion is fundamentally driven by continuous physical interaction with the environment. Whether walking, running, or simply standing, the forces exchanged between our feet and the ground provide crucial insights for understanding and reconstructing human movement. Recent advances in wearable insole devices offer a compelling solution for capturing these forces in diverse, real‐world scenarios. Sensor insoles pose no constraint on the users' motion (unlike mocap suits) and are unaffected by line‐of‐sight limitations (in contrast to optical systems). These qualities make sensor insoles an ideal choice for robust, unconstrained motion capture, particularly in outdoor environments. Surprisingly, leveraging these devices with recent motion reconstruction methods remains largely unexplored. Aiming to fill this gap, we present Step2Motion , the first approach to reconstruct human locomotion from multi‐modal insole sensors. Our method utilizes pressure and inertial data—accelerations and angular rates—captured by the insoles to reconstruct human motion. We evaluate the effectiveness of our approach across a range of experiments to show its versatility for diverse locomotion styles, from simple ones like walking or jogging up to moving sideways, on tiptoes, slightly crouching, or dancing. The complete source code, trained model, data, and supplementary material used in this paper can be found at: https://vcai.mpi-inf.mpg.de/projects/Step2Motion/

Abstract High‐quality motion reconstruction that follows the user's movements can be achieved by high‐end mocap systems with many sensors. However, obtaining such animation quality with fewer input devices is gaining popularity as it brings mocap closer to the general public. The main challenges include the loss of end‐effector accuracy in learning‐based approaches, or the lack of naturalness and smoothness in IK‐based solutions. In addition, such systems are often finely tuned to a specific number of trackers and are highly sensitive to missing data, e.g., in scenarios where a sensor is occluded or malfunctions. In response to these challenges, we introduce DragPoser, a novel deep‐learning‐based motion reconstruction system that accurately represents hard and dynamic constraints, attaining real‐time high end‐effectors position accuracy. This is achieved through a pose optimization process within a structured latent space. Our system requires only one‐time training on a large human motion dataset, and then constraints can be dynamically defined as losses, while the pose is iteratively refined by computing the gradients of these losses within the latent space. To further enhance our approach, we incorporate a Temporal Predictor network, which employs a Transformer architecture to directly encode temporality within the latent space. This network ensures the pose optimization is confined to the manifold of valid poses and also leverages past pose data to predict temporally coherent poses. Results demonstrate that DragPoser surpasses both IK‐based and the latest data‐driven methods in achieving precise end‐effector positioning, while it produces natural poses and temporally coherent motion. In addition, our system showcases robustness against on‐the‐fly constraint modifications, and exhibits adaptability to various input configurations and changes. The complete source code, trained model, animation databases, and supplementary material used in this paper can be found at https://upc-virvig.github.io/DragPoser

Interactive applications demand believable characters that respond naturally to dynamic environments. Traditional character animation techniques often struggle to handle arbitrary situations, leading to a growing trend of dynamically selecting motion-captured animations based on predefined features. While Motion Matching has proven effective for locomotion by aligning to target trajectories, animating environment interactions and crowd behaviors remains challenging due to the need to consider surrounding elements. Existing approaches often involve manual setup or lack the naturalism of motion capture. Furthermore, in crowd animation, body animation is frequently treated as a separate process from trajectory planning, leading to inconsistencies between body pose and root motion. To address these limitations, we present Environment-aware Motion Matching , a novel real-time system for full-body character animation that dynamically adapts to obstacles and other agents, emphasizing the bidirectional relationship between pose and trajectory. In a preprocessing step, we extract shape, pose, and trajectory features from a motion capture database. At runtime, we perform an efficient search that matches user input and current pose while penalizing collisions with a dynamic environment. Our method allows characters to naturally adjust their pose and trajectory to navigate crowded scenes.

Tactical elements are crucial in team sports. The analysis of hypothetical game situations greatly benefits from positional diagrams showing where the players are. These diagrams often show the layout of the players through simple symbols, which provide no information about their poses. This paper investigates if the visualization of player poses is benefitial for tactical understanding of positional diagrams in padel. We propose a realistic, cartoon-like representation of the players and discuss its integration into a typical positional diagram. To overcome the cost of generating player representations depicting their pose, we propose a method to generate such representations from minimal user input. We conducted a user study to evaluate the effectiveness of our pose-aware diagrams. The tasks for the study were designed to encompass the main in-game scenarios in padel, which include the ballholder at the net with opponents defending, the reverse situation, and transitions between these two states. We found that our representation is preferred over a symbolic one that only indicates player orientation. The proposed method enables coaches to produce such representations within a matter of seconds, thereby significantly facilitating the creation of detailed and easily analyzable depictions of game situations.

An increasing number of virtual reality applications require environments that emulate real-world conditions. These environments often involve dynamic virtual humans showing realistic behaviors. Understanding user perception and navigation among these virtual agents is key for designing realistic and effective environments featuring groups of virtual humans. While collision risk significantly influences human locomotion in the real world, this risk is largely absent in virtual settings. This paper studies the impact of the expected collision feedback on user perception and interaction with virtual crowds. We examine the effectiveness of commonly used collision feedback techniques (auditory cues and tactile vibrations) as well as inducing participants to expect that a physical bump with a real person might occur, as if some virtual humans actually correspond to real persons embodied into them and sharing the same physical space. Our results indicate that the expected collision feedback significantly influences both participant behavior—encompassing global navigation and local movements—and subjective perceptions of presence and copresence. Specifically, the introduction of a perceived risk of actual collision was found to significantly impact global navigation strategies and increase the sense of presence. Auditory cues had a similar effect on global navigation and additionally enhanced the sense of copresence. In contrast, vibrotactile feedback was primarily effective in influencing local movements.

Abstract In this paper, we discuss the requirements and technical challenges within the EHEM project, Enhancement of Heritage Experiences: The Middle Ages, an ongoing research program for the acquisition, analysis, documentation, interpretation, digital restoration, and communication of medieval artistic heritage. The project involves multidisciplinary teams comprising art historians and visual computing experts. Despite the vast literature on digital 3D models in support of Cultural Heritage, the field is so rich and diverse that specific projects often imply distinct, unique requirements which often challenge the computational technologies and suggest new research opportunities. As good representatives of such diversity, we describe the three monuments that serve as test cases for the project, all of them with a rich history of architecture and paintings. We discuss the art historians’ view of how digital models can support their research, the expertise and technological solutions adopted so far, as well as the technical challenges in multiple areas spanning geometry and appearance acquisition, color analysis and digital restitution, as well as the representation of the profound transformations due to the alterations suffered over the centuries.

Accurate and reliable human motion reconstruction is crucial for creating natural interactions of full-body avatars in Virtual Reality (VR) and entertainment applications. As the Metaverse and social applications gain popularity, users are seeking cost-effective solutions to create full-body animations that are comparable in quality to those produced by commercial motion capture systems. In order to provide affordable solutions though, it is important to minimize the number of sensors attached to the subject’s body. Unfortunately, reconstructing the full-body pose from sparse data is a heavily under-determined problem. Some studies that use IMU sensors face challenges in reconstructing the pose due to positional drift and ambiguity of the poses. In recent years, some mainstream VR systems have released 6-degree-of-freedom (6-DoF) tracking devices providing positional and rotational information. Nevertheless, most solutions for reconstructing full-body poses rely on traditional inverse kinematics (IK) solutions, which often produce non-continuous and unnatural poses. In this article, we introduce SparsePoser, a novel deep learning-based solution for reconstructing a full-body pose from a reduced set of six tracking devices. Our system incorporates a convolutional-based autoencoder that synthesizes high-quality continuous human poses by learning the human motion manifold from motion capture data. Then, we employ a learned IK component, made of multiple lightweight feed-forward neural networks, to adjust the hands and feet toward the corresponding trackers. We extensively evaluate our method on publicly available motion capture datasets and with real-time live demos. We show that our method outperforms state-of-the-art techniques using IMU sensors or 6-DoF tracking devices, and can be used for users with different body dimensions and proportions.

The use of self-avatars is gaining popularity thanks to affordable VR headsets. Unfortunately, mainstream VR devices often use a small number of trackers and provide low-accuracy animations. Previous studies have shown that the Sense of Embodiment, and in particular the Sense of Agency, depends on the extent to which the avatar's movements mimic the user's movements. However, few works study such effect for tasks requiring a precise interaction with the environment, i.e., tasks that require accurate manipulation, precise foot stepping, or correct body poses. In these cases, users are likely to notice inconsistencies between their self-avatars and their actual pose. In this paper, we study the impact of the animation fidelity of the user avatar on a variety of tasks that focus on arm movement, leg movement and body posture. We compare three different animation techniques: two of them using Inverse Kinematics to reconstruct the pose from sparse input (6 trackers), and a third one using a professional motion capture system with 17 inertial sensors. We evaluate these animation techniques both quantitatively (completion time, unintentional collisions, pose accuracy) and qualitatively (Sense of Embodiment). Our results show that the animation quality affects the Sense of Embodiment. Inertial-based MoCap performs significantly better in mimicking body poses. Surprisingly, IK-based solutions using fewer sensors outperformed MoCap in tasks requiring accurate positioning, which we attribute to the higher latency and the positional drift that causes errors at the end-effectors, which are more noticeable in contact areas such as the feet.

Digital color restitution aims to digitally restore the original colors of a painting. Existing image editing applications can be used for this purpose, but they require a select-and-edit workflow and thus they do not scale well to large collections of paintings or different regions of the same painting. To address this issue, we propose an automated workflow that requires only a few representative source colors and associated target colors as input from art historians. The system then creates a control grid to model a deformation of the CIELAB color space. Such deformation can be applied to arbitrary images of the same painting. The proposed approach is suitable for restituting the color of images from a large photographic campaign, as well as for the textures of 3D reconstructions of a monument. We demonstrate the benefits of our method on a collection of mural paintings from a medieval monument.

Herb Voelcker and his research team laid the foundations of Solid Modelling, on which Computer-Aided Design is based. He founded the ambitious Production Automation Project, that included Constructive Solid Geometry (CSG) as the basic 3D geometric representation. CSG trees were compact and robust, saving a memory space that was scarce in those times. But the main computational problem was Boundary Evaluation: the process of converting CSG trees to Boundary Representations (BReps) with explicit faces, edges and vertices for manufacturing and visualization purposes. This paper presents some glimpses of the history and evolution of some ideas that started with Herb Voelcker. We briefly describe the path from “localization and boundary evaluation” to “localization and printing”, with many intermediate steps driven by hardware, software and new mathematical tools: voxel and volume representations, triangle meshes, and many others, observing also that in some applications, voxel models no longer require Boundary Evaluation. In this last case, we consider the current research challenges and discuss several avenues for further research.

One of the most essential interactions in Virtual Reality (VR) is the user’s ability to move around and explore the virtual environment. The design of the navigation technique plays a crucial role in the user experience since it determines key usability aspects. VR devices allow for an immersive exploration of 3D worlds, but navigation in VR is challenging for many users, due to potential usability issues related to specific VR controllers, user skills, and motion sickness. Although hundreds of interaction techniques have been proposed for this task, VR navigation still poses a high entry barrier for many users. In this paper we argue that adapting the navigation technique to its context of use can lead to substantial improvements in navigation usability and accessibility. The context of use includes the type of scene, the available physical space, as well as the profile of the user. We present a test platform to facilitate the design and fine-tuning of interaction techniques for 3D navigation. We focus on mainstream VR devices (headsets and controllers) and support the most common navigation metaphors (walking, flying, teleportation). The key idea is to let developers specify, at runtime, the exact mapping between user actions and locomotion changes, for any of the supported metaphors. Such mappings are described by a collection of parameters (e.g. maximum speed) whose values can be adjusted interactively through a GUI, or be provided by user-defined code which can be edited at runtime. Feedback obtained from developers suggests that this approach can be used to quickly adapt the navigation techniques to various people including persons with no previous 3D navigation skills, elderly people, and people with disabilities, as well as to the type, size and semantics of the virtual environment.

High-end Terrestrial Lidar Scanners are often equipped with RGB cameras that are used to colorize the point samples. Some of these scanners produce panoramic HDR images by encompassing the information of multiple pictures with different exposures. Unfortunately, exported RGB color values are not in an absolute color space, and thus point samples with similar reflectivity values might exhibit strong color differences depending on the scan the sample comes from. These color differences produce severe visual artifacts if, as usual, multiple point clouds colorized independently are combined into a single point cloud. In this paper we propose an automatic algorithm to minimize color differences among a collection of registered scans. The basic idea is to find correspondences between pairs of scans, i.e. surface patches that have been captured by both scans. If the patches meet certain requirements, their colors should match in both scans. We build a graph from such pair-wise correspondences, and solve for the gain compensation factors that better uniformize color across scans. The resulting panoramas can be used to colorize the point clouds consistently. We discuss the characterization of good candidate matches, and how to find such correspondences directly on the panorama images instead of in 3D space. We have tested this approach to uniformize color across scans acquired with a Leica RTC360 scanner, with very good results.

Abstract The animation of user avatars plays a crucial role in conveying their pose, gestures, and relative distances to virtual objects or other users. Self‐avatar animation in immersive VR helps improve the user experience and provides a Sense of Embodiment. However, consumer‐grade VR devices typically include at most three trackers, one at the Head Mounted Display (HMD), and two at the handheld VR controllers. Since the problem of reconstructing the user pose from such sparse data is ill‐defined, especially for the lower body, the approach adopted by most VR games consists of assuming the body orientation matches that of the HMD, and applying animation blending and time‐warping from a reduced set of animations. Unfortunately, this approach produces noticeable mismatches between user and avatar movements. In this work we present a new approach to animate user avatars that is suitable for current mainstream VR devices. First, we use a neural network to estimate the user's body orientation based on the tracking information from the HMD and the hand controllers. Then we use this orientation together with the velocity and rotation of the HMD to build a feature vector that feeds a Motion Matching algorithm. We built a MoCap database with animations of VR users wearing a HMD and used it to test our approach on both self‐avatars and other users' avatars. Our results show that our system can provide a large variety of lower body animations while correctly matching the user orientation, which in turn allows us to represent not only forward movements but also stepping in any direction.

Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to inferapparentpersonality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.

Abstract The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette‐defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.

Abstract Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are low‐resolution except for selected places on Earth. In this paper we present a new method to turn low‐resolution DEMs into plausible and faithful high‐resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi‐resolution dictionaries), we benefit from high‐resolution aerial images to produce highly‐detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high‐resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.

Abstract Normal vectors are essential for many point cloud operations, including segmentation, reconstruction and rendering. The robust estimation of normal vectors from 3D range scans is a challenging task due to undersampling and noise, specially when combining points sampled from multiple sensor locations. Our error model assumes a Gaussian distribution of the range error with spatially‐varying variances that depend on sensor distance and reflected intensity, mimicking the features of Lidar equipment. In this paper we study the impact of measurement errors on the covariance matrices of point neighborhoods. We show that covariance matrices of the true surface points can be estimated from those of the acquired points plus sensor‐dependent directional terms. We derive a lower bound on the neighbourhood size to guarantee that estimated matrix coefficients will be within a predefined error with a prescribed probability. This bound is key for achieving an optimal trade‐off between smoothness and fine detail preservation. We also propose and compare different strategies for handling neighborhoods with samples coming from multiple materials and sensors. We show analytically that our method provides better normal estimates than competing approaches in noise conditions similar to those found in Lidar equipment.

State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.

Abstract In this survey we review, classify and compare existing approaches for real‐time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level‐of‐detail (LoD) rendering of animated characters, including polygon‐based, point‐based, and image‐based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo‐instancing, palette skinning, and dynamic key‐pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.

Abstract In this paper, we present an inexpensive approach to create highly detailed reconstructions of the landscape surrounding a road. Our method is based on a space‐efficient semi‐procedural representation of the terrain and vegetation supporting high‐quality real‐time rendering not only for aerial views but also at road level. We can integrate photographs along selected road stretches. We merge the point clouds extracted from these photographs with a low‐resolution digital terrain model through a novel algorithm which is robust against noise and missing data. We pre‐compute plausible locations for trees through an algorithm which takes into account perceptual cues. At runtime we render the reconstructed terrain along with plants generated procedurally according to pre‐computed parameters. Our rendering algorithm ensures visual consistency with aerial imagery and thus it can be integrated seamlessly with current virtual globes.

Abstract High‐quality texture minification techniques, including trilinear and anisotropic filtering, require texture data to be arranged into a collection of pre‐filtered texture maps called mipmaps. In this paper, we present a compression scheme for mipmapped textures which achieves much higher quality than current native schemes by exploiting image coherence across mipmap levels. The basic idea is to use a high‐quality native compressed format for the upper levels of the mipmap pyramid (to retain efficient minification filtering) together with a novel compact representation of the detail provided by the highest‐resolution mipmap. Key elements of our approach include delta‐encoding of the luminance signal, efficient encoding of coherent regions through texel runs following a Hilbert scan, a scheme for run encoding supporting fast random‐access, and a predictive approach for encoding indices of variable‐length blocks. We show that our scheme clearly outperforms native 6:1 compressed texture formats in terms of image quality while still providing real‐time rendering of trilinearly filtered textures.

ABSTRACT In this paper, we present a new impostor‐based representation for 3D animated characters supporting real‐time rendering of thousands of agents. We maximize rendering performance by using a collection of pre‐computed impostors sampled from a discrete set of view directions. Our approach differs from previous work on view‐dependent impostors in that we use per‐joint rather than per‐character impostors. Our characters are animated by applying the joint rotations directly to the impostors, instead of choosing a single impostor for the whole character from a set of pre‐defined poses. This offers more flexibility in terms of animation clips, as our representation supports any arbitrary pose, and thus, the agent behavior is not constrained to a small collection of pre‐defined clips. Because our impostors are intended to be valid for any pose, a key issue is to define a proper boundary for each impostor to minimize image artifacts while animating the agents. We pose this problem as a variational optimization problem and provide an efficient algorithm for computing a discrete solution as a pre‐process. To the best of our knowledge, this is the first time a crowd rendering algorithm encompassing image‐based performance, small graphics processing unit footprint, and animation independence is proposed. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract Rendering detailed animated characters is a major limiting factor in crowd simulation. In this paper we present a new representation for 3D animated characters which supports output‐sensitive rendering. Our approach is flexible in the sense that it does not require us to pre‐define the animation sequences beforehand, nor to pre‐compute a dense set of pre‐rendered views for each animation frame. Each character is encoded through a small collection of textured boxes storing colour and depth values. At runtime, each box is animated according to the rigid transformation of its associated bone and a fragment shader is used to recover the original geometry using a dual‐depth version of relief mapping. Unlike competing output‐sensitive approaches, our compact representation is able to recover high‐frequency surface details and reproduces view‐motion parallax effectively. Our approach drastically reduces both the number of primitives being drawn and the number of bones influencing each primitive, at the expense of a very slight per‐fragment overhead. We show that, beyond a certain distance threshold, our compact representation is much faster to render than traditional level‐of‐detail triangle meshes. Our user study demonstrates that replacing polygonal geometry by our impostors produces negligible visual artefacts.

Abstract Raster‐based topographic maps are commonly used in geoinformation systems to overlay geographic entities on top of digital terrain models. Using compressed texture formats for encoding topographic maps allows reducing latency times while visualizing large geographic datasets. Topographic maps encompass high‐frequency content with large uniform regions, making current compressed texture formats inappropriate for encoding them. In this paper we present a method for locally‐adaptive compression of topographic maps. Key elements include a Hilbert scan to maximize spatial coherence, efficient encoding of homogeneous image regions through arbitrarily‐sized texel runs, a cumulative run‐length encoding supporting fast random‐access, and a compression algorithm supporting lossless and lossy compression. Our scheme can be easily implemented on current programmable graphics hardware allowing real‐time GPU decompression and rendering of bilinear‐filtered topographic maps.

Abstract In this paper, we present an efficient approach for the interactive rendering of large‐scale urban models, which can be integrated seamlessly with virtual globe applications. Our scheme fills the gap between standard approaches for distant views of digital terrains and the polygonal models required for close‐up views. Our work is oriented towards city models with real photographic textures of the building facades. At the heart of our approach is a multi‐resolution tree of the scene defining multi‐level relief impostors. Key ingredients of our approach include the pre‐computation of a small set of zenithal and oblique relief maps that capture the geometry and appearance of the buildings inside each node, a rendering algorithm combining relief mapping with projective texture mapping which uses only a small subset of the pre‐computed relief maps, and the use of wavelet compression to simulate two additional levels of the tree. Our scheme runs considerably faster than polygonal‐based approaches while producing images with higher quality than competing relief‐mapping techniques. We show both analytically and empirically that multi‐level relief impostors are suitable for interactive navigation through large urban models.

The act of pointing to graphical elements is one of the fundamental tasks in Human-Computer Interaction. In this paper we analyze visual feedback techniques for accurate pointing on stereoscopic displays. Virtual feedback techniques must provide precise information about the pointing tool and its spatial relationship with potential targets. We show both analytically and empirically that current approaches provide poor feedback on stereoscopic displays, resulting in low user performance when accurate pointing is required. We propose a new feedback technique following a camera viewfinder metaphor. The key idea is to locally flatten the scene objects around the pointing direction to facilitate their selection. We present the results of a user study comparing cursor-based and ray-based visual feedback techniques with our approach. Our user studies indicate that our viewfinder metaphor clearly outperforms competing techniques in terms of user performance and binocular fusion.

Most pointing techniques for 3D selection on virtual environments rely on a ray originating at the user's hand whose direction is controlled by the hand orientation. In this paper we study the potential mismatch between visible objects (those which appear unoccluded from the user's eye position) and selectable objects (those which appear unoccluded from the user's hand position). We study the impact of such eye-hand visibility mismatch on selection performance, and propose a new technique for ray control which attempts to overcome this problem. We present an experiment to compare our ray control technique with classic raycasting in selection tasks with complex 3D scenes. Our user studies show promising results of our technique in terms of speed and accuracy.

Abstract We explore the automatic recovery of solids from their binary volumetric discretizations. In particular, we propose an approach, called Pressing, for smoothing isosurfaces extracted from binary volumes while recovering their large planar regions (flats). Pressing yields a surface that is guaranteed to contain the samples of the volume classified as interior and exclude those classified as exterior. It uses global optimization to identify flats and constrained bilaplacian smoothing to eliminate sharp features and high frequencies from the rest of the isosurface. It recovers sharp edges between flat regions and between flat and smooth regions. Hence, the resulting isosurface is usually a very accurate approximation of the original solid. Furthermore, the segmentation of the isosurface into flat and curved faces and the sharp/smooth labelling of their edges may be valuable for shape recognition, simplification, compression and various reverse engineering and manufacturing applications.

Abstract Relief impostors have been proposed as a compact and high‐quality representation for high‐frequency detail in 3D models. In this paper we propose an algorithm to represent a complex object through the combination of a reduced set of relief maps. These relief maps can be rendered with very few artifacts and no apparent deformation from any view direction. We present an efficient algorithm to optimize the set of viewing planes supporting the relief maps, and an image‐space metric to select a sufficient subset of relief maps for each view direction. Selected maps (typically three) are rendered based on the well‐known ray‐height‐field intersection algorithm implemented on the GPU. We discuss several strategies to merge overlapping relief maps while minimizing sampling artifacts and to reduce extra texture requirements. We show that our representation can maintain the geometry and the silhouette of a large class of complex shapes with no limit in the viewing direction. Since the rendering cost is output sensitive, our representation can be used to build a hierarchical model of a 3D scene.

Abstract The computation of the largest planar region approximating a 3D object is an important problem with wide applications in modeling and rendering. Given a voxelization of the 3D object, we propose an efficient algorithm to solve a discrete version of this problem. The input of the algorithm is the set of grid edges connecting the interior and the exterior of the object (called sticks). Using a voting‐based approach, we compute the plane that slices the largest number of sticks and is orientation‐compatible with these sticks. The robustness and efficiency of our approach rests on the use of two different parameterizations of the planes with suitable properties. The first of these is exact and is used to retrieve precomputed local solutions of the problem. The second one is discrete and is used in a hierarchical voting scheme to compute the global maximum. This problem has diverse applications that range from finding object signatures to generating simplified models. Here we demonstrate the merits of the algorithm for efficiently computing an optimized set of textured impostors for a given polygonal model. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling

Abstract The exploration of complex walkthrough models is often a difficult task due to the presence of densely occluded regions which pose a serious challenge to online navigation. In this paper we address the problem of algorithmic generation of exploration paths for complex walkthrough models. We present a characterization of suitable properties for camera paths and we discuss an efficient algorithm for computing them with little or no user intervention. Our approach is based on identifying the free‐space structure of the scene (represented by a cell and portal graph) and an entropy‐based measure of the relevance of a view‐point. This metric is key for deciding which cells have to be visited and for computing critical way‐points inside each cell. Several results on different model categories are presented and discussed.

This paper presents a new approach for generating coarse-level approximations of topologically complex models. Dramatic topology reduction is achieved by converting a 3D model to and from a volumetric representation. Our approach produces valid, error-bounded models and supports the creation of approximations that do not interpenetrate the original model, either being completely contained in the input solid or bounding it. Several simple to implement versions of our approach are presented and discussed. We show that these methods perform significantly better than other surface-based approaches when simplifying topologically-rich models such as scene parts and complex mechanical assemblies.

Occlusion culling and level‐of‐detail rendering have become two powerful tools for accelerating the handling of very large models in real‐time visualization applications. We present a framework that combines both techniques to improve rendering times. Classical occlusion culling algorithms compute potentially visible sets (PVS), which are supersets of the sets of visible polygons. The novelty of our approach is to estimate the degree of visibility of each object of the PVS using synthesized coarse occluders. This allows to arrange the objects of each PVS into several Hardly‐Visible Sets (HVS) with similar occlusion degree. According to image accuracy and frame rate requirements, HVS provide a way to avoid sending to the graphics pipeline those objects whose pixel contribution is low due to partial occlusion. The image error can be bounded by the user at navigation time. On the other hand, as HVS offer a tighter estimation of the pixel contribution for each scene object, it can be used for a more convenient selection of the level‐of‐detail at which objects are rendered. In this paper, we describe the new framework technique, provide details of its implementation using a visibility octree as the chosen occlusion culling data structure and show some experimental results on the image quality.

Abstract The paper focuses on automatic simplification algorithms for the generation of a multiresolution family of solid models from an initial boundary representation of a polyhedral solid. An algorithm for general polyhedra based on an intermediate octree representation is proposed. Simplified elements of the multiresolution family approximate the initial solid within increasing tolerances. A discussion among different octree‐based simplification methods and the standard marching cubes algorithm is presented.