Xiaopeng Wei^{1, 2}, Xiaoyong Fang^{1, 2}, Qiang Zhang² and Dongsheng Zhou^{1, 2}

¹ School of Mechanical and Engineering, Dalian University of Technology,
Dalian, 116024 China
xpwei@dlu.edu.cn, paperxyfang@gmail.com

² Key Laboratory of Advanced Design and Intelligent Computing (Dalian University),
Ministry of Education, Dalian, 116622, China
Zhangq26@126.com
 

Abstract. We propose a new method for matching two 3D point sets of identical cardinality with global similarity but local non-rigid deformations and distribution errors. This problem arises from marker based optical motion capture (Mocap) systems for facial Mocap data. To establish one-to-one identifications, we introduce a forward 3D point pattern matching (PPM) method based on spatial geometric flexibility, which considers a non-rigid deformation between the two point-sets. First, a model normalization algorithm based on simple rules is presented to normalize the two point-sets into a fixed space. Second, a facial topological structure model is constructed, which is used to preserve spatial information for each FP. Finally, we introduce a Local Deformation Matrix (LDM) to rectify local searching vector to meet the local deformation. Experimental results confirm that this method is applicable for robust 3D point pattern matching of sparse point sets with underlying non-rigid deformation and similar distribution.