Spatio-temporal Segmentation for the Similarity Measurement of Deforming Meshes
Contributors
- Guoliang Luo, Frederic Cordier, Hyewon Seo (University of Strasbourg)
- Wei Zeng, Wenqiang Xie (Jiangxi Normal University, China)
- Guoliang Luo, Xin Zhao (East China Jiaotong University, China)
- Zhigang Deng (Univ. Houston, USA), Xiaogang Jin (Zhejiang University, China)
Abstract
Although there have been a large body of works on computing the similarity of static shapes, similarity judgments on deforming meshes are not studied well. In this study, we investigate a similarity measurement method for comparing two deforming meshes. Our algorithm uses the degree of deformation to binarily label each triangle in deforming mesh in the spatio-temporal domain, which is then encoded in a form of evolving graphs (EG) to obtain a compact representation of the given motion. Based on EG representation, we further developed a motion similarity measure between two deforming meshes, which we formulate as a graph matching problem. In this formulation, we have identified, and proposed solution to, two challenging problems: First, comparison of graphs that change their topologies over time is a difficult problem, for which few attempts have been made. Second, the evolving graph representation tend to be noisy.
More recently, we have searched for an exploitation of the spatio-temporal coherency within a segment towards a compact represent an animation mesh. Given a mesh on which a motion-driven spatio-temporal segmentation has been computed, we perform PCA-based compression on each spatio-temporal segment. Since our algorithm is designed to exploit both temporal and spatial segmentation redundancies by adaptively determining segmentation boundaries, it shows a significantly better performance over other comparable compression methods, as expected.
