【作者】 翁建广；

【导师】 潘云鹤； 庄越挺；

【作者基本信息】 浙江大学 ， 计算机应用， 2005， 博士

【摘要】 随着多媒体、动画、CAD等技术的发展,三维模型的数量急剧增长。由于本身不具有文本描述信息,而且三维形状的复杂度高,所以基于内容的三维形状检索成为新的研究热点。在检索技术支持下,基于实例的形状创新具有重要的意义,形状过渡作为实例形变的一种常用方法,可以提高产品设计、动画制作等领域的创造力。由于三维模型表达的多样性,通过统一的体模型表达提高检索和过渡算法的适用性,是形状创新的必要手段。 为实现基于体模型的检索,首先需要实体体素化方法把常见的网格模型快速转变为体模型,本文提出基于启发式种子填充的网格模型误差受限二值实体体素化方法。在效率、鲁棒性和误差等方面因素达到平衡,克服传统的实体体素化方法或效率太低、或对网格模型的质量要求过高或结果模型误差失控的局限性。体素化分三个步骤:初步二值模型(PBVM)、边界二值体模型(BBVM)和修正二值体模型(RBVM)。RBVM以PBVM和RBVM为基础提取启发知识,在利用种子填充的鲁棒性的同时,显著提高了种子填充的速度。 为提高检索算法的通用性,在实体体素化的辅助下,本文提出体积比特征向量提取方法。为克服网格模型表示精度和观察方位对检索效果的影响,在特征向量提取之前首先进行连续主成分分析达到方位规范化的目的。通过把模型空间在三个坐标系方向的递归等分,提取模型的在不同层次的体积比,而后通过分层加权计算形状相似度。为提高检索效果,本文提出单权重相关反馈和先序满意度评价两种权重调整方式。基于特征向量的提取机制,本文提出了快速的特征索引和预过滤机制。 为实现基于距离场体模型的的形状过渡,本文提出了基于快进法的网格模型距离场转换方法。首先通过超覆盖曲面体素化算法建立快进法的参照区域,而后通过快进法以6连通窄带的方式进行快进扩散,在扩散过程中对窄带中的候选点采用堆排序选择距离最小体素。在快进计算过程中,记录每个体素的参照体素,实现完全距离场表示。 为处理拓扑异构的模型,本文采用水平集方法进行形状过渡,并对稀疏场算法进行了改进,突破了活动集必须覆盖零水平集的束缚,提出了单侧活动集定义,并采用拓扑关系定义外层体素集。在减少活动体素量的同时,由于单侧活动集在6连通意义上的单层性,也提高了外层体素集的更新速度。为克服稀疏场算法对欧式距离的近似计算而造成的走样,本文提出了均值平移和窄带回退两种平滑算法。更多还原

【Abstract】 The number of 3D models is increasing rapidly along with the development of multimedia, animation and CAD technologies. Content-based 3D shape retrieval is a research focus because 3D models lack text information and there are usually complex. Aided by retrieval technologies, case-based shape innovation is a common way in some applications, e.g., product design and amination invention. To improve the applicability, it is necessary to unifiy the shape representation during retrieval and morphing computation.A binary voxelization method, i.e., error-bounded soild voxelization for polygonal models based on heuristic seed filling, is proposed to aid volume-based retrieval. The method is a balance among robust, efficient and error. It sovles different problems of other solid voxelization methods, such problems including low efficiency, impratical requirement to polygonal models and lacking means to control error. The method consists of three stages: Primary Binary Volume Model (PBVM), Boundary Binary Volume Model (BBVM), Revised Binary Volume Model (RBVM). RBVM discovers heuristic knowledge from PBVM and BBVM. Heuristic seed filling improves efficiency remarkably and keeps the robustness of conventional seed filling.To improve the applicability of retrieval, volume-ratio is used to extract feature vector. Continue principal components analysis is applied to polygonal models to normalize postion before feature vector computation. Volume ratio is computed by bin-subdividing the bounding box of the polygonal model along axes recursively. Based on such volume ratios, similarity is obtained by weighted sum. Two feedback mechanisms are proposed to improve retrieval result, including single weight feedback and first order statisfication evaluation. Based on feature vector computation, efficient feature indexing is propsed as well and pre-filtering is used to improve retrieval speed.To do distance-based morphing, distance transformation is proposed based on fast marching method. At first, supercover voxelization is appled to continue surface to install reference zone. The distance field is propagated from reference zone by 6-conntected narrow band and heap sorting is used to select minimal distance from all candidate voxels in the propagating process. The reference of each voxel is recorded to setup the complete distance field representation which is the basis for computation of higher resosultion distance fields.Level-set method is adopted to do morphing since its ability to deal with shapes with different topological properties. Single-sided active set is proposed to improve the efficency of sparse field algorithm, which is a fast numerical solution of level-set. Other layer sets are defined by topology accordingly. Such definitaions not only decrease the number of active voxels but give a more efficient way to update other layer set voxels. Two smoothing methods, i.e. averaging&translation and narrow band evolving&back, is proposed to complement the alias arised by approximate computation of Euclidean distance in the evolving process.更多还原