Computing the Inner Distances of Volumetric Models for Articulated Shape Description with a Visibility Graph

Authors:
Yu-Shen Liu;Karthik Ramani;Min Liu
Affiliations:
Tsinghua University, Beijing;Purdue University, West Lafayette;Tsinghua University, Beijing
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2011

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Abstract

A new visibility graph-based algorithm is presented for computing the inner distances of a 3D shape represented by a volumetric model. The inner distance is defined as the length of the shortest path between landmark points within the shape. The inner distance is robust to articulation and can reflect the deformation of a shape structure well without an explicit decomposition. Our method is based on the visibility graph approach. To check the visibility between pairwise points, we propose a novel, fast, and robust visibility checking algorithm based on a clustering technique which operates directly on the volumetric model without any surface reconstruction procedure, where an octree is used for accelerating the computation. The inner distance can be used as a replacement for other distance measures to build a more accurate description for complex shapes, especially for those with articulated parts. The binary executable program for the Windows platform is available from https://engineering.purdue.edu/PRECISE/VMID.