3D model representation and manipulation based on skeletonization

Abstract

3D model is a promising type of multimedia content for entertainment, research and education purposes. This thesis addresses the representation and manipulation of 3D models based on skeletonization, which is a commonly used technique to extract a compact descriptor and effectively capture the topological and geometric structure of 3D models. My research focuses on the refinement of skeletonization and its applications in 3D model matching, retrieval, and decomposition. By introducing a framework based on Scale-Space-Filtering (SSF), I integrate both the global node significance and the local chain-coded structure for pose-aware model retrieval; then adopt the topological mapping scheme for skeleton-based model decomposition. Experiment and comparison with state-of-art work on benchmark databases demonstrate the accuracy and efficiency of this framework.