三维网格拓扑感知模型及应用研究
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摘要
随着三维模型扫描设备和交互式建模工具的快速发展和广泛使用，以三维模型为表现形式的第四代数字媒体——数字几何，比二维图像具有更丰富的视觉感知细节和内容，更适用于人类的视觉感知和思维模式，在实际应用中被广泛采用。三维数字几何的数据越来越庞大，要完整地呈现真实的虚拟场景和实时地绘制这些三维数字几何实体是一件相当困难的事情。基于视觉感知理论的三维模型处理理论与技术可在保证视觉感知质量的前提下，能较大幅度地提高三维场景的渲染性能，已成为计算机图形学界的研究热点，为三维数字几何处理与应用提供了一种有效的途径。
视觉感知理论的核心之一是提供可计算的视觉感知模型，主要集中在视觉注意机制、视觉掩蔽效应和拓扑知觉理论三个方面。在三维数字几何处理技术中，主要体现为三维网格显著图、粗糙图和骨架提取。然而，现有研究主要侧重于人类视觉系统的单一视觉感知特性，缺乏通用的视觉感知处理模型和三维模型视觉质量及视觉降质的客观评估方法。具有视觉意义的三维模型分割是视觉感知理论的重要组成部分，基于马尔科夫随机场的分割可较好地对三维模型的视觉先验知识和几何特征进行建模。目前，基于马尔科夫随机场实现具有视觉意义的三维模型分割的研究还处于起步阶段，缺乏易于集成不同特征的基于马尔科夫随机场的三维网格分割通用框架。本文针对上述研究热点中存在的问题展开研究，论文的主要研究工作和创新性如下：
（1）提出了一种三维网格拓扑感知图通用计算模型——SMTPM（SkeletonModulated Topological Perception Map）。从人类视觉系统的视觉注意机制、视觉掩蔽效应和全局拓扑感知三大特性出发，提出了自底向上的改进的显著度图谱模型以及面向全局拓扑感知特性和运动重要性的基于骨架提取技术的骨架图谱模型。以骨架图谱对三维模型的显著度图谱和粗糙度图谱进行调制，提出了一种通用的视觉感知处理模型。该模型分别在不同的退化情况下可描述HVS的视觉注意机制和视觉掩蔽效应特性。
（2）基于三维网格拓扑感知图通用计算模型，提出了一种适于三维模型简化过程中的多尺度视觉降质评估算法，并用于简化算法的比较。考虑SMTPM在退化为显著度图谱时的情形，以香农信息熵为工具，提出了面向结构相似性视觉降质的全局感知结构化降质评估方法，实现了多尺度的结构化视觉降质度量和三维模型简化算法的比较。通过构建主观实验评测库和设计主观实验评测协议，实验结果分析表明，该评估算法要优于传统的几何度量方法，并具有多尺度评估的特性，适于多分辨率和多层次细节等应用。
（3）基于三维网格拓扑感知图通用计算模型，提出了一种视点快速选择方法，定义了用于三维模型视觉质量评价的感觉熵。基于SMTPM模型，提出了视点感知信息量、最佳视点和最不利视点的定义及计算方法，并基于子分模板设计了一种迭代式快速视点选择算法。同时，还基于视点感知信息量定义了球形视点感知信息谱，为所有三维模型的视觉感知信息提供了统一规范的表达方式。基于球形视点感知信息谱，本文定义了感觉熵，为三维模型处理提供了通用的视觉质量度量和理论指导。
（4）提出了基于马尔科夫随机场的三维模型统一分割框架，并基于骨架图和分割框架实现了具有视觉意义的分割。基于马尔科夫随机场构建了一种通用的三维模型分割框架，并测试了其通用性。马尔科夫随机场聚类是该框架的核心，三维模型顶点等基元的任何特征均可很方便地集成到该框架中。基于该分割框架，将视觉先验知识等引入其中，提出了一种具有视觉意义的三维模型分割方法，实现了具有视觉意义的三维模型分割。实验表明基于该分割框架实现的分割算法在分割效果上要优于其他聚类算法。
With the fast development and widely spread of3D scanner and interactive modelingtools, digital geometry with the expression of3D models, as the fourth generation digitalmedia is widely used and becoming and is more suitable for human visual system(HVS)and thinking pattern for their abundant visually perception details and contents than2Ddigital image. The increasing size of3D digital geometry data make it a indeed difcultthing to perform real-time description and rendering of a virtual scene. The visual per-ception based3D object processing theory and technology can significantly improve therender performance and visual experience of a3D scene under the condition of the samevisual quality and has become a eye-catching topic. They provide an efcient tool for3Ddigital geometry processing and application.
The kernel of visual perceptual processing theory aims to provide a visual perceptionbased computational model, it mainly focuses on the following three aspects: visual atten-tion, visual masking and topology perception. They are in the form of3D mesh saliencymap, roughness map and sekelton respectively. However, existing research works fail toconsider the overall visual perception characteristics and lack of general visual perceptionmodel with corresponding objective visual quality and degradation evaluation methods.Visual meaningful3D mesh segmentation is an important part of visual perception theory,3D model segmentation based on Markov Random Field(MRF)provides an efcient mod-eling tool for integrating the geometric features with visual prior knowledge. However,visually meaningful based3D mesh segmentation using MRF is in its beginning stage andshort of a MRF based segmentation framework having the capability to integrate diferentfeatures. Aiming at the above issues in reserarch hot topics, the main research works andcontributions of this dissertation are as the following:
(1) A general visual perception based computational model–SMTPM(Skeleton Mod-ulated Topological Perception Map) is proposed. Considering the attention mechanism,masking mechanism and global topological perception of HVS, an improved bottom-upattention oriented saliency map model is presented. A global topology perception orient-ed and kinematics importance guided skeleton map algorithm is also proposed. Based onmodulation between saliency and roughness maps using the skeleton map, We propose a general visual perception model which can describe the attention and masking mecha-nisms of HVS under diferent degradation status.
(2) A multi-scale visual degradation evaluation algorithm based on SMTPM is p-resented and used to compare mesh simplification methods. Considering the situationof SMTPMs′degeneration to saliency map, a global perceptual degradation evaluationmethod based on Shannons′information entropy is proposed and used to evaluate the vi-sual degradation during mesh simplification and compare mesh simplification methods.The subjective corpus and subjective experiment protocol are also designed. The statisti-cal analysis results for conducted subjective experiments demonstrate that the subjectiveexperiment protocol is valid and the evaluation method is superior to other geometricbased metrics. The capability of multi-scale visual degradation evaluation is also verified.
(3) A rapid viewpoint selection method and a definition of perceptual entropy areproposed. Based on SMTPM, this paper presents the definitions for the amount of view-point perceptual information. The best and worst viewpoint selection methods based onsubdivision stencil are also proposed. Additionally, a Spherical Viewpoint PerceptualInformation Map(SVPIM) is defined based on the amount of viewpointsp′erceptual in-formation. SVPIM provids a uniform expression for visual perceptual information ofarbitrary3D objects. The perceptual entropy is first proposed using SVPIM and provide atheortical guide and general visual perception quality evaluation for3D object processing.
(4) Based on the skeleton map and a unified segmentation framework using MRFpresented in this paper, a new visual meaningful mesh segementation is achieved. MRFbased clustering is the kernel of this framework and any3D mesh item with geometric orperceptual features can be easily integrated into the framework. A new visual meaningfulmesh segmentation algorithm is implementated by introducing the visual prior knowledgeto the framework and utilizing the double level Gibbs random field. Experimental result-s show that segmentation algorithms based on the proposed framework achieves bettersegmentation results than other clustering based segmentation algorithms.
视觉感知理论的核心之一是提供可计算的视觉感知模型，主要集中在视觉注意机制、视觉掩蔽效应和拓扑知觉理论三个方面。在三维数字几何处理技术中，主要体现为三维网格显著图、粗糙图和骨架提取。然而，现有研究主要侧重于人类视觉系统的单一视觉感知特性，缺乏通用的视觉感知处理模型和三维模型视觉质量及视觉降质的客观评估方法。具有视觉意义的三维模型分割是视觉感知理论的重要组成部分，基于马尔科夫随机场的分割可较好地对三维模型的视觉先验知识和几何特征进行建模。目前，基于马尔科夫随机场实现具有视觉意义的三维模型分割的研究还处于起步阶段，缺乏易于集成不同特征的基于马尔科夫随机场的三维网格分割通用框架。本文针对上述研究热点中存在的问题展开研究，论文的主要研究工作和创新性如下：
（1）提出了一种三维网格拓扑感知图通用计算模型——SMTPM（SkeletonModulated Topological Perception Map）。从人类视觉系统的视觉注意机制、视觉掩蔽效应和全局拓扑感知三大特性出发，提出了自底向上的改进的显著度图谱模型以及面向全局拓扑感知特性和运动重要性的基于骨架提取技术的骨架图谱模型。以骨架图谱对三维模型的显著度图谱和粗糙度图谱进行调制，提出了一种通用的视觉感知处理模型。该模型分别在不同的退化情况下可描述HVS的视觉注意机制和视觉掩蔽效应特性。
（2）基于三维网格拓扑感知图通用计算模型，提出了一种适于三维模型简化过程中的多尺度视觉降质评估算法，并用于简化算法的比较。考虑SMTPM在退化为显著度图谱时的情形，以香农信息熵为工具，提出了面向结构相似性视觉降质的全局感知结构化降质评估方法，实现了多尺度的结构化视觉降质度量和三维模型简化算法的比较。通过构建主观实验评测库和设计主观实验评测协议，实验结果分析表明，该评估算法要优于传统的几何度量方法，并具有多尺度评估的特性，适于多分辨率和多层次细节等应用。
（3）基于三维网格拓扑感知图通用计算模型，提出了一种视点快速选择方法，定义了用于三维模型视觉质量评价的感觉熵。基于SMTPM模型，提出了视点感知信息量、最佳视点和最不利视点的定义及计算方法，并基于子分模板设计了一种迭代式快速视点选择算法。同时，还基于视点感知信息量定义了球形视点感知信息谱，为所有三维模型的视觉感知信息提供了统一规范的表达方式。基于球形视点感知信息谱，本文定义了感觉熵，为三维模型处理提供了通用的视觉质量度量和理论指导。
（4）提出了基于马尔科夫随机场的三维模型统一分割框架，并基于骨架图和分割框架实现了具有视觉意义的分割。基于马尔科夫随机场构建了一种通用的三维模型分割框架，并测试了其通用性。马尔科夫随机场聚类是该框架的核心，三维模型顶点等基元的任何特征均可很方便地集成到该框架中。基于该分割框架，将视觉先验知识等引入其中，提出了一种具有视觉意义的三维模型分割方法，实现了具有视觉意义的三维模型分割。实验表明基于该分割框架实现的分割算法在分割效果上要优于其他聚类算法。
With the fast development and widely spread of3D scanner and interactive modelingtools, digital geometry with the expression of3D models, as the fourth generation digitalmedia is widely used and becoming and is more suitable for human visual system(HVS)and thinking pattern for their abundant visually perception details and contents than2Ddigital image. The increasing size of3D digital geometry data make it a indeed difcultthing to perform real-time description and rendering of a virtual scene. The visual per-ception based3D object processing theory and technology can significantly improve therender performance and visual experience of a3D scene under the condition of the samevisual quality and has become a eye-catching topic. They provide an efcient tool for3Ddigital geometry processing and application.
The kernel of visual perceptual processing theory aims to provide a visual perceptionbased computational model, it mainly focuses on the following three aspects: visual atten-tion, visual masking and topology perception. They are in the form of3D mesh saliencymap, roughness map and sekelton respectively. However, existing research works fail toconsider the overall visual perception characteristics and lack of general visual perceptionmodel with corresponding objective visual quality and degradation evaluation methods.Visual meaningful3D mesh segmentation is an important part of visual perception theory,3D model segmentation based on Markov Random Field(MRF)provides an efcient mod-eling tool for integrating the geometric features with visual prior knowledge. However,visually meaningful based3D mesh segmentation using MRF is in its beginning stage andshort of a MRF based segmentation framework having the capability to integrate diferentfeatures. Aiming at the above issues in reserarch hot topics, the main research works andcontributions of this dissertation are as the following:
(1) A general visual perception based computational model–SMTPM(Skeleton Mod-ulated Topological Perception Map) is proposed. Considering the attention mechanism,masking mechanism and global topological perception of HVS, an improved bottom-upattention oriented saliency map model is presented. A global topology perception orient-ed and kinematics importance guided skeleton map algorithm is also proposed. Based onmodulation between saliency and roughness maps using the skeleton map, We propose a general visual perception model which can describe the attention and masking mecha-nisms of HVS under diferent degradation status.
(2) A multi-scale visual degradation evaluation algorithm based on SMTPM is p-resented and used to compare mesh simplification methods. Considering the situationof SMTPMs′degeneration to saliency map, a global perceptual degradation evaluationmethod based on Shannons′information entropy is proposed and used to evaluate the vi-sual degradation during mesh simplification and compare mesh simplification methods.The subjective corpus and subjective experiment protocol are also designed. The statisti-cal analysis results for conducted subjective experiments demonstrate that the subjectiveexperiment protocol is valid and the evaluation method is superior to other geometricbased metrics. The capability of multi-scale visual degradation evaluation is also verified.
(3) A rapid viewpoint selection method and a definition of perceptual entropy areproposed. Based on SMTPM, this paper presents the definitions for the amount of view-point perceptual information. The best and worst viewpoint selection methods based onsubdivision stencil are also proposed. Additionally, a Spherical Viewpoint PerceptualInformation Map(SVPIM) is defined based on the amount of viewpointsp′erceptual in-formation. SVPIM provids a uniform expression for visual perceptual information ofarbitrary3D objects. The perceptual entropy is first proposed using SVPIM and provide atheortical guide and general visual perception quality evaluation for3D object processing.
(4) Based on the skeleton map and a unified segmentation framework using MRFpresented in this paper, a new visual meaningful mesh segementation is achieved. MRFbased clustering is the kernel of this framework and any3D mesh item with geometric orperceptual features can be easily integrated into the framework. A new visual meaningfulmesh segmentation algorithm is implementated by introducing the visual prior knowledgeto the framework and utilizing the double level Gibbs random field. Experimental result-s show that segmentation algorithms based on the proposed framework achieves bettersegmentation results than other clustering based segmentation algorithms.
引文
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