Recent advancements in diffusion models have significantly enhanced the performance of text-to-image models in image synthesis. To enable control over the the spatial locations of the generated objects, diffusion-based methods typically utilize object layout as an auxiliary input. However, we observe that this approach treats all objects as being on the same layer and neglect their visibility order, leading to the synthesis of overlapping objects with incorrect occlusions. To address this limitation, we introduce in this paper a new training-free framework that considers object visibility order explicitly and allows users to place overlapping objects in a stack of layers. Our framework consists of two visibility-based designs. First, we propose a novel Sequential Denoising Process (SDP) to divide the whole image generation into multiple stages for different objects, each stage primarily focuses on an object. Second, we propose a novel Visibility-Order-Aware (VOA) Loss to transform the layout and occlusion constraints into an attention map optimization process to improve the accuracy of synthesizing object occlusions in complex scenes. By merging these two novel components, our framework, dubbed VODiff, enables the generation of photorealistic images that satisfy user-specified spatial constraints and object occlusion relationships. In addition, we introduce VOBench, a diverse benchmark dataset containing 200 curated samples, each with a reference image, text prompts, object visibility orders and layout maps. We conduct extensive evaluations on this dataset to demonstrate the superiority of our approach.

This paper proposes a novel neural network architecture dubbed Cross-Modal Integrative Feature Network (CMIFN) to address three challenges on sketch-based 3D shape retrieval. Firstly, existing methods, like those based on multi-view CNNs, mostly capture surface visual features, ignoring internal geometry features. CMIFN integrates both multi-view and geometry features of 3D objects, consequently extracting a comprehensive global feature. Secondly, existing methods often manipulate sketches to enhance them, which may introduce superfluous data. Utilising an attention mechanism, CMIFN keeps redundancy in check while achieving a more accurate sketch representation. Thirdly, existing methods often compare the distance between sketches and 3D shapes in the same feature space without considering their inherent differences, which can lead to sub-optimal retrieval results. CMIFN introduces a modality-weighted classifier module, which assigns different weights to features from different modalities, creating a shared feature space to minimize the gap between similar objects across modalities thus increase the retrieval accuracy. Our comprehensive experiments have demonstrated CMIFN’s state-of-the-art performance on benchmark datasets.

While existing image‑guided composition methods may help insert a foreground object onto a user-specified region of a background image, achieving natural blending inside the region with the rest of the image unchanged, we observe that these existing methods often struggle in synthesizing seamless interaction-aware compositions when the task involves human-object interactions. In this paper, we first propose HOComp, a novel approach for compositing a foreground object onto a human-centric background image, while ensuring harmonious interactions between the foreground object and the background person and their consistent appearances. Our approach includes two key designs: (1) MLLMs-driven Region-based Pose Guidance (MRPG), which utilizes MLLMs to identify the interaction region as well as the interaction type (e.g., holding and lefting) to provide coarse-to-fine constraints to the generated pose for the interaction while incorporating human pose landmarks to track action variations and enforcing fine-grained pose constraints; and (2) Detail-Consistent Appearance Preservation (DCAP), which unifies a shape-aware attention modulation mechanism, a multi-view appearance loss, and a background consistency loss to ensure consistent shapes/textures of the foreground and faithful reproduction of the background human. We then propose the first dataset, named Interaction-aware Human-Object Composition (IHOC), for the task. Experimental results on our dataset show that HOComp effectively generates harmonious human-object interactions with consistent appearances, and outperforms relevant methods qualitatively and quantitatively.

ABSTRACT To achieve efficient querying of BIM products in large‐scale virtual scenes, this study introduces a Weisfeiler‐Lehman (WL) kernel augmented representation for Building Information Modeling(BIM) products based on Product Attributed Graphs (PAGs). Unlike conventional data‐driven approaches that demand extensive labeling and preprocessing, our method directly processes raw BIM product data to extract stable semantic and geometric features. Initially, a PAG is constructed to encapsulate product features. Subsequently, a WL kernel enhanced multi‐channel node aggregation strategy is employed to integrate BIM product attributes effectively. Leveraging the bijective relationship in graph isomorphism, an unsupervised convergence mechanism based on attribute value differences is established. Experiments demonstrate that our method achieves convergence within an average of 3 iterations, completes graph isomorphism testing in minimal time, and attains an average query accuracy of 95%. This approach outperforms 1‐WL and 3‐WL methods, especially in handling products with topologically isomorphic but oppositely attributed spaces.

In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in complex buildings and incorporates robotic navigation and intelligent path planning. A novel simulated human behavior model was integrated, capable of complex human–robot interaction, independent escape route searching, and exhibiting herd mentality and memory mechanisms. We also proposed a multiagent framework that combines MARL and ARL to enhance overall evacuation efficiency and robustness. Additionally, we developed a new ARL evaluation framework that provides a novel method for quantifying agents’ performance. Various experiments of differing difficulty levels were conducted, and the results demonstrate that the proposed framework exhibits advantages in emergency evacuation scenarios. Specifically, our ARLR approach increased survival rates by 1.8% points in low-difficulty evacuation tasks compared to the RLR approach using only MARL algorithms. In high-difficulty evacuation tasks, the ARLR approach raised survival rates from 46.7% without robots to 64.4%, exceeding the RLR approach by 1.7% points. This study aims to enhance the efficiency and safety of human–robot collaborative fire evacuations and provides theoretical support for evaluating and improving the performance and robustness of ARL agents.

In this article, we propose the first geometry and texture (double) referenced interactive two-dimensional (2D) and 3D caricature generating and editing method. The main challenge of caricature generation lies in the fact that it not only exaggerates the facial geometry but also refreshes the facial texture. We address this challenge by utilizing the semantic segmentation maps as an intermediary domain, removing the influence of photo texture while preserving the person-specific geometry features. Specifically, our proposed method consists of two main components: 3D-CariNet and CariMaskGAN. 3D-CariNet uses sketches or caricatures to exaggerate the input photo into several types of 3D caricatures. To generate a CariMask, we geometrically exaggerate the photos using the projection of exaggerated 3D landmarks, after which CariMask is converted into a caricature by CariMaskGAN. In this step, users can edit and adjust the geometry of caricatures freely. Moreover, we propose a semantic detail preprocessing approach that considerably increases the details of generated caricatures and allows modification of hair strands, wrinkles, and beards. By rendering high-quality 2D caricatures as textures, we produce 3D caricatures with a variety of texture styles. Extensive experimental results have demonstrated that our method can produce higher-quality caricatures as well as support interactive modification with ease.

Existing GAN-based approaches to caricature generation mainly focus on exaggerating a character’s global facial structure. This often leads to the failure in highlighting significant facial features such as big eyes and hook nose. To address this limitation, we propose a new approach termed as G2L-CariGAN, which uses feature maps of spatial dimensions instead of latent codes for geometric exaggeration. G2L-CariGAN first exaggerates the global facial structure of the character on a low-dimensional feature map and then exaggerates its local facial features on a high-dimensional feature map. Moreover, we develop a caricature identity loss function based on feature maps, which well retains the character's identity after exaggeration. Our experiments have demonstrated that G2L-CariGAN outperforms the state-of-arts in terms of the quality of exaggerating a character and retaining its identity.

With the advent of Web3.0 and Metaverse technologies, there is a growing interest in integrating web and metaverse environments to create immersive, cross-platform "Webverse" experiences. Despite active research in this area, the lack of standardized testing approaches has hindered the development of robust Webverse applications. This paper addresses this gap by proposing a comprehensive framework for testing Web-Metaverse integrations. We analyze existing Webverse platforms, characterize different stages and scenes through network data analysis, and design a suite of standardized tests. Furthermore, we implement a one-stop benchmarking tool tailored for web-based metaverse applications. The fidelity of our tool is validated through comparisons with human testing results. To demonstrate its applicability, we utilize the proposed testing process to evaluate and compare Webverse implementations on popular platforms like Galacean and Mozilla Hubs, deployed on their respective servers. The outcomes highlight platform-specific differences and the efficacy of our standardized approach in enabling seamless Web-Metaverse convergence.

With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of lightweighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache management, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.

In recent years, with the rapid development of mobile Internet and Web3D technologies, a large number of web-based online 3D visualization applications have emerged. Web3D applications, including Web3D online tourism, Web3D online architecture, Web3D online education environment, Web3D online medical care, and Web3D online shopping are examples of these applications that leverage 3D rendering on the web. These applications have pushed the boundaries of traditional web applications that use text, sound, image, video, and 2D animation as their main communication media, and resorted to 3D virtual scenes as the main interaction object, enabling a user experience that delivers a strong sense of immersion. This paper approached the emerging Web3D applications that generate stronger impacts on people's lives through “real-time rendering technology”, which is the core technology of Web3D. This paper discusses all the major 3D graphics APIs of Web3D and the well-known Web3D engines at home and abroad and classify the real-time rendering frameworks of Web3D applications into different categories. Finally, this study analyzed the specific demand posed by different fields to Web3D applications by referring to the representative Web3D applications in each particular field. Our survey results show that Web3D applications based on real-time rendering have in-depth sectors of society and even family, which is a trend that has influence on every line of industry.

In this paper, we propose CariPainter, the first interactive caricature generating and editing method. The main challenge of caricature generation lies in the fact that it not only exaggerates the facial geometry but also refreshes the facial texture. We solve this challenging problem by utilizing the semantic segmentation maps as an intermediary domain, removing the influence of photo texture while preserving the person-specific geometry features. Specifically, our proposed method consists of two main components: CariSketchNet and CariMaskGAN. CariSketchNet exaggerates the photo segmentation map to construct CariMask. Then, CariMask is converted into a caricature by CariMaskGAN. In this step, users can edit and adjust the geometry of caricatures freely. Additionally, we propose a semantic detail pre-processing approach, which considerably increases details of generated images and allows modification of hair strands, wrinkles, and beards. Extensive experimental results show that our method produces higher-quality caricatures as well as supports easily used interactive modification.

Abstract With the mobile technology continues to grow and evolve, the technology of presenting building information modeling (BIM) with an online platform has become an important application in the fields of civil engineering, architecture, and computer visualization. However, due to network bandwidth and browser performance limitations, it was difficult to display large‐scale BIM scenes in a flawless manner on mobile browsers. CEBOW, a Cloud‐Edge‐Browser Online architecture for visualizing BIM components with online solutions, is proposed in this article. The method combines transmission scheduling, cache management, and optimal initial loading into a single system architecture. For network transmission testing, BIM scenes are used, and the results show that our method effectively reduces scene loading time and networking delay while improving the visualization effect of large‐scale scenes.

Abstract The increased growth of building complexity confronts the limited device resources, especially for lightweight personal digital devices. The research of handling these complex buildings with limited resources has become a pivotal research topic. This paper explores the task of lightweight indoor path planning in complex building information modeling (BIM) buildings. Both the environment modeling and path searching are important addressed through indoor path planning with a lightweight web‐based approach. A lightweight framework is designed in which the complex indoor path planning of a whole BIM building can be simplified into many local path searching of the building's interior spaces and all the online computation is decreased within no more than two simpler ones. In addition, an automatically indoor environment modeling method is proposed to partition the BIM building into many local interior spaces, constructing their spatial relationship and local grid map in a multilayered network. In the end, a heuristics optimized A star algorithm is implemented to speed up the shortest local path searching for the case of the path ends in a narrow corner. The final experiment shows that the online computational cost can be reduced greatly and dynamically to an average of 2 s when searching for the shortest path in a complex BIM building.

In this article, we present a deep learning approach to sketch-based shape retrieval that incorporates a few novel techniques to improve the quality of the retrieval results. First, to address the problem of scarcity of training sketch data, we present a sketch augmentation method that more closely mimics human sketches compared to simple image transformation. Our method generates more sketches from the existing training data by (i) removing a stroke, (ii) adjusting a stroke, and (iii) rotating the sketch. As such, we generate a large number of sketch samples for training our neural network. Second, we obtain the 2D renderings of each 3D model in the shape database by determining the view positions that best depict the 3D shape: i.e., avoiding self-occlusion, showing the most salient features, and following how a human would normally sketch the model. We use a convolutional neural network (CNN) to learn the best viewing positions of each 3D model and generates their 2D images for the next step. Third, our method uses a cross-domain learning strategy based on two Siamese CNNs that pair up sketches and the 2D shape images. A joint Bayesian measure is used to measure the output similarity from these CNNs to maximize inter-class similarity and minimize intra-class similarity. Extensive experiments show that our proposed approach comprehensively outperforms many existing state-of-the-art methods.

There are many bottlenecks that limit the computing power of the Mobile Web3D and they need to be solved before implementing a public fire evacuation system on this platform. In this study, we focus on three key problems: (1) The scene data for large-scale building information modeling (BIM) are huge, so it is difficult to transmit the data via the Internet and visualize them on the Web; (2) The raw fire dynamic simulator (FDS) smoke diffusion data are also very large, so it is extremely difficult to transmit the data via the Internet and visualize them on the Web; (3) A smart artificial intelligence fire evacuation app for the public should be accurate and real-time. To address these problems, the following solutions are proposed: (1) The large-scale scene model is made lightweight; (2) The amount of dynamic smoke is also made lightweight; (3) The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method. We propose a real-time fire evacuation system based on the ant colony optimization (RFES-ACO) algorithm with reused dynamic pheromones. Simulation results show that the public could use Mobile Web3D devices to experience fire evacuation drills in real time smoothly. The real-time fire evacuation system (RFES) is efficient and the evacuation rate is better than those of the other two algorithms, i.e., the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.

Platforms for interior DIY(Do It Yourself) design should hold sufficient realistic sense and light manual operation in interior modeling, besides more flexibility and adaptation of online editing are also essential. But current pure Web3D or pure image based platforms are hardly meet those goals. Therefore this paper presents a lightweight and immersive solutions by editing virtual 3D furniture into captured 2D interior pictures interactively, placing them at the optimal location automatically and rendering them in real time to consistently harmonize them with real interior pictures in terms of geometric layout and lighting visual effects. Our contributions consist in: (1)cuboid modeling from camera captured interior pictures interactively without loss of realistic sense and with light manual operation; (2)lightweight automatic furniture arrangement method with enough flexibility and adaptation for online editing; (3)lightweight IBL(Image Based Lighting) and PBR(Physic Based Rendering) to make virtual furniture immerse into real interior image more visually authentic. Compared with those existing online systems, this solution can provide low-cost, convenient, pervasive online services for interior DIY design over mobile Internet.

Abstract We propose a framework for statistical modeling of the 3D geometry and topology of botanical trees. We treat botanical trees as points in a tree‐shape space equipped with a proper metric that captures the geometric and the topological differences between trees. Geodesics in the tree‐shape space correspond to the optimal sequence of deformations, i.e. bending, stretching, and topological changes, which align one tree onto another. In this way, the 3D tree modeling and synthesis problem becomes a problem of exploring the tree‐shape space either in a controlled fashion, using statistical regression, or randomly by sampling from probability distributions fitted to populations in the tree‐shape space. We show how to use this framework for (1) computing statistical summaries, e.g. the mean and modes of variations, of a population of botanical trees, (2) synthesizing random instances of botanical trees from probability distributions fitted to a population of botanical trees, and (3) modeling, interactively, 3D botanical trees using a simple sketching interface. The approach is fast and only requires as input 3D botanical tree models with a known upright orientation.

We propose an algorithm for generating novel 3D tree model variations from existing ones via geometric and structural blending. Our approach is to treat botanical trees as elements of a tree-shape space equipped with a proper metric that quantifies geometric and structural deformations. Geodesics, or shortest paths under the metric, between two points in the tree-shape space correspond to optimal deformations that align one tree onto another, including the possibility of expanding, adding, or removing branches and parts. Central to our approach is a mechanism for computing correspondences between trees that have different structures and a different number of branches. The ability to compute geodesics and their lengths enables us to compute continuous blending between botanical trees, which, in turn, facilitates statistical analysis, such as the computation of averages of tree structures. We show a variety of 3D tree models generated with our approach from 3D trees exhibiting complex geometric and structural differences. We also demonstrate the application of the framework in reflection symmetry analysis and symmetrization of botanical trees.

We propose Cloud Baking, a collaborative rendering architecture for dynamic Web3D scenes. In our architecture, the cloud renderer renders the scene with the global illumination (GI) information in a GI map; the web-based client renderer renders the scene with ambient lighting only and blends it with the GI map received from the cloud for the final scene. This approach allows the users to interact with the web scene and change the scene dynamically through the web interface end, yet move the computationally heavy tasks of global illumination computation to the cloud. A challenge we face is the interaction delay that causes the frames rendered on the cloud and the client to go out of sync. We propose to use 3D warping and a hole-filling algorithm designed for GI map to predict the late GI map. We show both quantitatively and visually the quality of the GI map produced using our method. Our prediction algorithm allows us to further reduce the frequency at which the GI map is computed and sent from the server, reducing both computational needs and bandwidth usage.

Current cloud rendering systems are expensive in terms of data storage, rendering computation, and networking transmission. In this paper, we propose a novel framework for lightweight and realistic WebSIM rendering based on cloud baking. Different from the existing cloud rendering systems that render full-frame image sequences, we propose that global illumination (GI) maps are baked occasionally at the server side. Our proposed framework consists of three key stages. First, the GI maps are re-baked only when the users change the WebSIM scene or the lighting. Then, after re-baking, these GI maps with indirect illumination are encoded and transferred to the client's web browsers using H.264. Finally, these GI maps are decoded and blended to produce rather realistic illuminating effects with WebGL direct illuminations. Compared with cloud rendering techniques, our system is lightweight, as it consumes much fewer resources at the cloud server in terms of both data and rendering. It provides a highly efficient, high-quality, and low-cost solution to WebSIM online interactive rendering. A prototype has been implemented, and we showed that it is able to achieve real-time rendering performance and satisfactory visual effects on web browsers, similar to popular offline rendering engines.

This paper explores how the reconstruction of special history scenario will be applied in online education. After investigating various virtual reality techniques including design of virtual educational system, reconstruction of virtual scene, management of scene, AI and light shadow rendering, we build an online education platform for touring a web3D virtual battlefield scenario called Huangyangjie in China. We firstly present the solution and scheme for rebuilding the web 3D battlefield Scenario using lightweight 3D models. Secondly, we present voxel of interesting (VOI) scene management strategy. Thirdly, we optimize A* algorithm in AI management process. Finally, we design an experiment for comparing virtual reality based technique teaching mode with traditional teaching mode.

Abstract The neighbor table/distributed hash table (DHT) is used to choose the data supplier for data‐dispatching services in distributed virtual environments based on peer‐to‐peer networks. It is essential that a stable and efficient neighbor table/DHT be maintained. Because the avatar has much freedom to roam, the spatial distribution of nodes is not uniform, and the logical topology may change dramatically. Therefore, traditional construction mechanisms, such as the neighbor‐discovery mechanism based on spatial distance or DHT, may involve fierce churn in the neighbor table and frequent message exchanges. In this paper, we proposed a dynamic node‐organizing mechanism that aims to solve these challenging problems by applying the avatar's behavioral characteristics to the neighbor maintenance mechanism and scene data transmission. First, we have summarized the common social behaviors of avatars and extracted their characteristics. We then propose an interest‐similarity measuring algorithm to divide the node into diverse clusters. Next, we measure the cluster stability in terms of interest entropy while constructing a stable neighbor mesh for each node in a cluster. We have conducted extensive simulation experiments that simulate avatar behaviors in a popular massively multiplayer online game. The results show that our proposed mechanism achieved a substantial alleviation of neighbor churn and reduced information exchange, which improves the transmission efficiency in distributed virtual environments. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Accessing Web3D contents is relatively slow through Internet under limited bandwidth. Preprocessing of 3D models can certainly alleviate the problem, such as 3D compression and progressive meshes (PM). But none of them considers the similarity between components of a 3D model, so that we could take advantage of this to further improve the efficiency. This paper proposes a similarity‐aware data reduction method together with PM, called lightweight progressive meshes (LPM). LPM aims to excavate similar components in a 3D model, generates PM representation of each component left after removing redundant components, and organizes all the processed data using a structure called lightweight scene graph. The proposed LPM possesses four significant advantages. First, it can minimize the file size of 3D model dramatically without almost any precision loss. Because of this, minimal data is delivered. Second, PM enables the delivery to be progressive, so called streaming. Third, when rendering at client side, due to lightweight scene graph, decompression is not necessary and instanced rendering is fully exerted. Fourth, it is extremely efficient and effective under very limited bandwidth, especially when delivering large 3D scenes. Performance on real data justifies the effectiveness of our LPM, which improves the state‐of‐the‐art in accessing Web3D contents. Copyright © 2015 John Wiley & Sons, Ltd.

Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm. However, standard single-way hierarchical methods are fundamentally flawed in that the errors in early steps cannot be corrected and accumulate. In this work, we propose a novel multi-branch hierarchical segmentation approach that alleviates such problems by learning multiple merging strategies in each step in a complementary manner, such that errors in one merging strategy could be corrected by the others. Our approach achieves the state-of-the-art performance for both object proposal and object detection tasks, comparing to previous object proposal methods.

Large-scale scenes' processing has become the major trend today. We mainly address the online walkthrough of Large-scale underground (UG) scenes in this paper. Taking into account the characteristics of UG scene, we first propose a lightweight preprocessing to optimize the raw UG scene and unify the raw data with scene, sub-scene and simple model. Then we generate a three-layered grid structure for organizing the scene to facilitate the visibility culling and data accessing. Finally, we design two scene management strategies, named SOI-Exterior Shell and Portal-Interior Shell, and integrate our methods in an experimental prototype. The experimental result shows that our method can remove a large amount of redundancies from the raw data, reduce resource consumption greatly and make it possible to walkthrough in large-scale UG scenes online without any web browsers plugins.

We present a framework for optimizing the streaming of large 3D applications to allow an instant interaction independently from network bandwidth or available processing power of the rendering unit. The instantaneous display of imperfect content that gets continuously refined is state of the art for many application areas on the web, why we propose to utilize this idea for interactive 3D web applications. To achieve this, two new approaches were chosen. In addition to a progressive transmission of geometry and texture data, we firstly propose an overall rating function to control the priorities between different media transmission blocks. Secondly, we use a similarity metric to use already loaded scene parts as a replacement of parts with lower priority. Our approach utilizes a client-server architecture to collect, convert, control, and transmit the scene data at runtime to allow an instant distribution of adapted data, while also reducing the CPU load and network traffic.

In this paper, we present a novel 3D model alignment method by analyzing the voxels of 3D meshes and a visual similarity based 3D model matching and retrieving method using active tabu search. Firstly, each 3D model is voxelized and applied voxels based PCA transformation, then it is represented by six depth images which are projected by rendering in the PCA coordinate system. Hybrid descriptors are extracted from these depth images to represent the origin 3D model shape features. Matching and retrieving is performed when geometric manifold entropy based active tabu search is used to index all the models in the library by its associated sets of depth images, then the dissimilarity between 3D models are computed from this indexed depth images dataset. Finally, in order to accelerate our proposed approach, all the key operations were implemented on GPU platform using its high parallel architecture. Experimental results show that our proposed method achieve better shape matching effect and gain absolutely improvement in retrieval performances on the Princeton 3D Shape Benchmark database.

Computing the planar sections of objects is a fundamental operation in solid modeling. Subdivision method is commonly used for solving such intersection problems. In this paper, a revolute quadric decomposition is proposed for surfaces of revolution, which are subdivided into a set of coaxial revolute quadrics along the generatrix. This reduces the intersection problem of a plane and a surface of revolution to the intersection problem of a plane and a revolute quadric, which has robust, accurate and efficient geometric solution. Further, the intersection curves can be represented with a group of G (G. Baciu et al., 2001) conic arcs. A new concept, valid intersection interval (VII), is introduced and a new technique, cylindrical bounding shell clipping, is proposed for efficient intersection detection for a plane and a surface of revolution. Finally, a tracing algorithm is presented for recognizing singular points and closed loops of intersection curves. Implemented examples show the robustness and effectiveness of the proposed algorithm.