【作者】 徐凯；

【导师】 熊岳山；

【作者基本信息】 国防科学技术大学 ， 计算机科学与技术， 2011， 博士

【摘要】 数字几何处理技术的快速发展对三维几何模型这一新兴数字媒体的发展和应用起到了巨大的推动作用。随着模型数量和质量的不断提高,如何分析和理解三维模型中蕴含的语义信息,以便更加有效地处理和使用三维模型,以及利用语义信息合成新的三维模型,逐渐成为几何处理领域所关注的最新热点。目前,数字几何处理研究正由低层次的几何属性度量与分析逐步转向高层次的面向语义的结构分析与处理上来。形状语义描述了人类关于物体外形、结构、功能以及它们之间联系的知识。形状分析是将形状知识与几何处理技术相结合,通过智能分析和学习等手段从三维模型中自动提取形状语义知识的过程。在形状分析方面,本文首先基于对称性与形状语义的关联,研究了基于对称性的单个模型有意义分割。然后基于同类物体功能部件的语义一致性,研究了同类物体的三维模型集合的一致分割。基于分析得到的语义信息,本文研究了两种语义驱动的三维几何建模方法。论文主要创新点包括:1.局部内蕴反射对称性分析:对称性是连接三维形状低层几何属性和高层语义信息的桥梁。因此,对称性分析是形状分析的核心问题之一。以往的工作研究了全局外蕴、局部外蕴和全局内蕴对称的检测。局部内蕴对称在三维几何体中更具一般性,其检测对形状分析具有重要意义。但由于参数化表示的复杂性,局部内蕴对称的检测非常困难。本文首次研究了闭合2-流形上局部内蕴反射对称的检测问题。基于极大内蕴反射对称生成集,给出了闭合2-流形上局部内蕴反射对称的定义。并以此为基础,提出了一种基于选举策略的内蕴反射对称变换的计算方法。为显式地计算离散曲面上的局部内蕴反射对称轴,提出了一种迭代式区域生长算法。基于得到的反射对称轴及相应的对称生成集,本文研究了基于内蕴反射对称的三维模型有意义分割。2.模型集的联合语义分析:一组同类物体的三维模型比单个模型包含更丰富的语义信息:同类物体往往共享相同的功能部件。因此,对同类物体模型集合的一致分割可以反映该组模型的功能部件分解与对应,这对于分析和理解模型的结构和功能具有重要意义。本文首次研究了同类人造物体的三维模型集合的联合语义分析,提出了一种基于形状风格分析的联合分析框架。该框架将形状风格定义为模型部件的各向异性尺度,基于无监督学习方法对输入模型集合进行形状风格聚类,然后通过风格类内模型的一致分割以及类间模型的部件对应,实现了模型“风格”(功能部件的相对尺度比例)与“内容”(模型的几何外形和部件构成等)的分离,并得到输入模型集合的一致分割。3.基于风格转移的模型自动合成:本文提出了基于形状风格转移的几何模型自动合成算法。该方法以联合分析后的同类物体模型集为输入,通过对应部件尺度缩放实现模型之间的形状风格转移,以自动生成新的三维模型。尺度缩放可能会导致模型各部件的分离,因此,我们研究了部件的重新组装和拼接。为实现部件的重新对准,本文提出了一种基于连接点集的迭代式优化算法。为实现部件之间的自然拼接,提出了一种连接曲线敏感的部件连接方法。4.图像启发的数据驱动几何建模:基于联合分析得到的模型集语义信息,本文进一步提出了图像启发的数据驱动几何建模方法。该方法由单幅图像中的物体得到建模启发,并以预先经过语义分析的候选三维模型集为基础,通过生成候选模型集的几何变种,得到与图像中物体相近的三维模型。在预处理阶段,我们首先对候选模型集进行联合分析和基于形变控制单元的结构约束分析。为实现基于图像的三维建模,本文提出了模型驱动的交互式图像分割方法,基于图像标注分割信息的候选模型检索方法,以及二维轮廓驱动下的三维模型结构保持形变算法。本文提出的建模方法具有以下特点:1)充分利用候选模型集的结构信息,解决了基于单幅图像的三维重建中的二义性问题;2)结构保持形变使结果模型继承了候选模型的结构信息,该信息可直接用于后续编辑和造型,因此建模结果具有立即可用的特性;3)结构保持使结果模型具有视点无关的结构一致性。更多还原

【Abstract】 The rapid development of digital geometry processing technique has been greatly boosting the growth of digital geometry. As a newly emerging digital multimedia, geometry is populating the internet with a remarkably speed. The significant growing of digital geometry in terms of both quantity and quality calls for more effective ways of processing and utilization. To this end, geometry processing is currently moving towards high-level shape analysis and understanding, aiming at discovering the underlying semantic information of a 3D shape. This gives the birth of the recent trend of high-level geometry processing, which has been widely recognized by the graphics community.Shape semantics reflects human knowledge about shape’s geometry, structure, functionality, and their relationship. The goal of shape analysis is to automatically extract such knowledge from a shape with the help of both geometry processing and input knowledge. Effective integration of the input shape knowledge is important to shape analysis. We first study the symmetry analysis of a single shape and develop symmetry-driven mesh segmentation for structural analysis. We then study the consistent analysis of semantics of a 3D model set. The pre-analyzed semantics greatly facilitate 3D modeling driven by a model set. The main contributions of this thesis include:1. Detection of partial intrinsic reflectional symmetry. Symmetry bridges the gap between low level geometry and high level semantics. Therefore, symmetry analysis is one of the central problems of shape analysis. Previous works have studied the detection of global extrinsic, partial extrinsic, and global intrinsic symmetries. Partial intrinsic symmetry is more general in 3D shapes, although its detection is more difficult due to the complexity of its parametric representation. This thesis, for the first time, studies the problem of automatic detection of partial intrinsic reflectional symmetry (PIRS) on a closed 2-manifold. We begin with a formal definition of PIRS on a closed 2-manifold using the maximal generating set of intrinsic reflectional symmetry. Based on the definition, we propose a robust, voting-based detection algorithm for PIRS over a 3D triangle mesh. To explicitly extract a set of intrinsic reflectional symmetry axis (IRSA) curves, we propose an iterative grass-fire region growing method. With the IRSA curves and their corresponding regions of generating set, we achieve symmetry-aware segmentation.2. Co-analysis of a model set. A set of models belonging to the same class often contains more semantic information than a single one; they often share the same functional parts, which may help better understanding the structure of the shape class. We study the problem of co-analysis of a set of man-made objects belonging to a certain class and present a framework for shape style analysis. Shape style is defined based on the anisotropic part scales. We perform an unsupervised style clustering. Through intra-style co-segmentation and inter-style part correspondence, we achieve style-content separation for the input shapes, where content refers to part composition and geometry and style represents scale proportion between functional parts. With such separation, we arrive at a consistent segmentation of the input set.3. Shape synthesis based on style transfer. With the consistent segmentation of a set of 3D shapes, we propose shape synthesis by style transfer between any two shapes in the set. Style transfer is achieved by part scaling according to the part correspondence implied by consistent segmentation. However, part scaling may detach neighboring parts. To re-align and stitch the detached neighboring parts, we propose an iterative re-alignment approach based joining point set between the two parts, as well as a joining curve aware method for natural part stitching.4. Photo-inspired data-driven shape modeling. Co-analysis augments a 3D model set with rich semantics. With the help of this, we introduce an algorithm for 3D object modeling where the modeling inspiration is drawn from an object captured in a single photograph. The modeling process is supported by an available set of candidate models, which have been pre-analyzed to possess useful high-level structural information. Our method creates a digital 3D model as a geometric variation from the candidate that best resembles target object in the photograph. To facilitate modeling from image, we propose image-space object segmentation, candidate model retrieval using labeled segmentation of the input image, together with a silhouette driven structure-preserving shape deformation. The main features of our method are three folds. First, the whole modeling pipeline makes heavy use of the semantics pre-analyzed for the candidate set, which helps to compensate for the ill-posedness of the 2D-to-3D reconstruction from a single image. Second, the structural information is preserved by the geometric variation so that the final product is coherent with its inherited structural information readily usable for subsequent model refinement. Third, also due to structure preservation, the resulting 3D model, although built from a single view, is structurally coherent from all views.更多还原