Skeleton Tree based Non-rigid 3D Shape Retrieval

Abstract

We propose a skeleton tree based method for classifying and retrieving non-rigid shapes. Firstly, based on the extracted skeletons of a non-rigid shape and geodesic distance computation, the center point in skeleton is defined and detected. Then, a skeleton tree is constructed based on the connection between the center point and other discrete points in skeleton. After that, a correspondence between the skeleton tree and the area distribution of the non-rigid shape is established. The skeleton tree features are achieved. The advantages of our method can be summarized as follows: (1) Scale-Invariant; (2) Low computational complexity; (3) Automatic topology repair. The experimental results show that our method is more accurate than existing methods.