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3D Inner-distance, which is defined as the length of the shortest path between points within 3D shape, is robust to articulation, especially in non-rigid 3D shapes; thus it can be used to construct the robust 3D shape feature descriptor. With the increasing data scale, the time consumption has become a problem in extracting the 3D inner-distances. In order to accelerate the process of extracting the 3D inner-distances, we propose a new method by using a hierarchical graph to compute the inner-distances. In this paper, we first put forward an adaptive-voxelization method based on Octree structure to construct a hierarchical graph; then the multi-level process of extracting 3D inner-distance is thus proposed.

A Multi-level Method for Fast Computing of All-Pairs 3D Inner-Distance