随着软硬件技术的飞速发展，三维数据开始广泛应用于工作生活中，三维模型的种类与数量也以前所未有的速度快速增长，如何对这些海量三维数据进行高效精准的快速检索已经成为当前计算机图形学、模式识别等领域中的一个研究热点，而非刚性三维形状检索则是其中最具挑战性的难题之一。本项目拟基于三维模型的标准形态与几何形状的稀疏特性对非刚性三维形状检索中的预处理、特征表示与形状匹配方法开展深入研究，提出新的基于显著视图的水密三维网格自动构建方法；应用流体力学与应力分析理论，提出新的特征保留的三维标准形态生成方法；结合三维形状与二维视图的高层语义和底层几何信息，对三维模型进行姿态不变的稀疏描述与精确匹配；并开发出相应的原型系统，实现对海量非刚性三维模型的快速精确检索。本项目的研究将推动非刚性三维形状分析处理技术与三维形状稀疏编码理论的发展，促进三维模型检索与三维信息重用技术的推广和使用，具有重要的理论与应用价值。

With the development of technologies in computer hardware and software, 3D models have become widely-used in our daily life. The category and number of 3D models are also increasing rapidly in a speed never seen before. Now, how to implement efficient and accurate retrieval for those enormous amounts of 3D models has become a research hotpot in areas of Computer Graphics, Pattern Recognition, etc. Non-rigid 3D shape retrieval is one of the most challenging tasks in this research direction. Based on the canonical form of 3D objects and the sparsity of geometric shapes, in this project we will conduct deep investigations for the preprocessing, feature representation and shape matching methods in non-rigid 3D shape retrieval. Specifically, we will design a salient view based method to automatically construct watertight 3D meshes. Also, we will apply hydrodynamics and stress analyzing theories to generate feature-preserving 3D canonical forms. Meanwhile, we will combine the high-level semantic and low-level geometric information of 3D shapes and 2D views to achieve pose-invariant sparse description and accurate matching for 3D models. Finally, the corresponding prototype system will be developed so that enormous amounts of non-rigid 3D models could be retrieved quickly and accurately. Researches of this project will promote the development of non-rigid 3D shape analysis and processing techniques and sparsity-based 3D shape representation and coding theories, and boost the utilization of 3D object searching and 3D information reusing technologies. Thus, our researches are of great importance in both theories and real applications.