Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.展开更多
Many graphics and computer-aided design applications require that the polygonal meshes used in geometric computing have the properties of not only 2-manifold but also are orientable. In this paper, by collecting previ...Many graphics and computer-aided design applications require that the polygonal meshes used in geometric computing have the properties of not only 2-manifold but also are orientable. In this paper, by collecting previous work scattered in the topology and geometry literature, we rigorously present a theoretical basis for orientable polygonal surface representation from a modem point of view. Based on the presented basis, we propose a new combinatorial data structure that can guarantee the property of orientable 2-manifolds and is primal/dual efficient. Comparisons with other widely used data structures are also presented in terms of time and space efficiency.展开更多
Various techniques have been developed and introduced to address the pressing need to create three-dimensional(3D)content for advanced applications such as virtual reality and augmented reality.However,the intricate n...Various techniques have been developed and introduced to address the pressing need to create three-dimensional(3D)content for advanced applications such as virtual reality and augmented reality.However,the intricate nature of 3D shapes poses a greater challenge to their representation and generation than standard two-dimensional(2D)image data.Different types of representations have been proposed in the literature,including meshes,voxels and implicit functions.Implicit representations have attracted considerable interest from researchers due to the emergence of the radiance field representation,which allows the simultaneous reconstruction of both geometry and appearance.Subsequent work has successfully linked traditional signed distance fields to implicit representations,and more recently the triplane has offered the possibility of generating radiance fields using 2D content generators.Many articles have been published focusing on these particular areas of research.This paper provides a comprehensive analysis of recent studies on implicit representation-based 3D shape generation,classifying these studies based on the representation and generation architecture employed.The attributes of each representation are examined in detail.Potential avenues for future research in this area are also suggested.展开更多
Recently unstructured dense point sets have become a new representation of geometric shapes. In this paper we introduce a novel framework within which several usable error metrics are analyzed and the most basic prope...Recently unstructured dense point sets have become a new representation of geometric shapes. In this paper we introduce a novel framework within which several usable error metrics are analyzed and the most basic properties of the pro- gressive point-sampled geometry are characterized. Another distinct feature of the proposed framework is its compatibility with most previously proposed surface inference engines. Given the proposed framework, the performances of four representative well-reputed engines are studied and compared.展开更多
Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D sh...Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,etc.The performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all applications.Therefore,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different applications.We also present existing datasets in these representations and further discuss future research directions.展开更多
基金Supported by the National Key R&D Program of China(2022YFC3803600).
文摘Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.
文摘Many graphics and computer-aided design applications require that the polygonal meshes used in geometric computing have the properties of not only 2-manifold but also are orientable. In this paper, by collecting previous work scattered in the topology and geometry literature, we rigorously present a theoretical basis for orientable polygonal surface representation from a modem point of view. Based on the presented basis, we propose a new combinatorial data structure that can guarantee the property of orientable 2-manifolds and is primal/dual efficient. Comparisons with other widely used data structures are also presented in terms of time and space efficiency.
基金supported by National Natural Science Foundation of China(No.62322210)Beijing Municipal Natural Science Foundation for Distinguished Young Scholars(No.JQ21013)Beijing Municipal Science and Technology Commission(No.Z231100005923031).
文摘Various techniques have been developed and introduced to address the pressing need to create three-dimensional(3D)content for advanced applications such as virtual reality and augmented reality.However,the intricate nature of 3D shapes poses a greater challenge to their representation and generation than standard two-dimensional(2D)image data.Different types of representations have been proposed in the literature,including meshes,voxels and implicit functions.Implicit representations have attracted considerable interest from researchers due to the emergence of the radiance field representation,which allows the simultaneous reconstruction of both geometry and appearance.Subsequent work has successfully linked traditional signed distance fields to implicit representations,and more recently the triplane has offered the possibility of generating radiance fields using 2D content generators.Many articles have been published focusing on these particular areas of research.This paper provides a comprehensive analysis of recent studies on implicit representation-based 3D shape generation,classifying these studies based on the representation and generation architecture employed.The attributes of each representation are examined in detail.Potential avenues for future research in this area are also suggested.
文摘Recently unstructured dense point sets have become a new representation of geometric shapes. In this paper we introduce a novel framework within which several usable error metrics are analyzed and the most basic properties of the pro- gressive point-sampled geometry are characterized. Another distinct feature of the proposed framework is its compatibility with most previously proposed surface inference engines. Given the proposed framework, the performances of four representative well-reputed engines are studied and compared.
基金supported by the National Natural Science Foundation of China(61828204,61872440)Beijing Municipal Natural Science Foundation(L182016)+2 种基金Youth Innovation Promotion Association CAS,CCF-Tencent Open FundRoyal Society Newton Advanced Fellowship(NAF\R2\192151)the Royal Society(IES\R1\180126)。
文摘Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,etc.The performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all applications.Therefore,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different applications.We also present existing datasets in these representations and further discuss future research directions.
