VMID: A Program for Volumetric Model Inner Distances (VMID)

Yu-Shen Liu    Karthik Ramani    Min Liu

School of Mechanical Engineering
School of Electrical Computer Engineering (by courtesy)
Purdue University, West Lafayette, IN, 47907, USA




A new method 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 well deformation of shape structure 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 and it operates directly on the volumetric model without any surface reconstruction procedure, where an octree is used for accelerating computation. The inner distance can be used as a replacement for other distance measures to build more accurate description for complex shapes, especially for those with articulated parts.





  Yu-Shen Liu, Karthik Ramani, Min Liu. Computing the inner  distances of volumetric models for articulated shape    description with a visibility graph. IEEE Transactions on  Pattern Analysis and Machine Intelligence (PAMI), 2011,   23(12): 2538-2544.




We would like to thank Dr. Tao Ju and Mr. Sasakthi Abeysinghe for providing the code for inputting the volumetric models during our work. Yi Fang also discussed some applications in molecular shape comparison. The models used in this paper were provided by the AIM@SHAPE project and Ran Gal. This material is partly based upon work supported by the National Science Foundation under Grant IIS No. 0535156. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.