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Motion Segmentation for Time-Varying Mesh Sequences Based on Spherical Registration

Abstract

A highly accurate motion segmentation technique for time-varying mesh (TVM) is presented. In conventional approaches, motion of the objects was analyzed using shape feature vectors extracted from TVM frames. This was because it was very difficult to locate and track feature points in the objects in the 3D space due to the fact that the number of vertices and connection varies each frame. In this study, we developed an algorithm to analyze the objects' motion in the 3D space using the spherical registration based on the iterative closest-point algorithm. Rough motion tracking is conducted and the degree of motion is robustly calculated by this method. Although the approach is straightforward, much better motion segmentation results than the conventional approaches are obtained by yielding such high precision and recall rates as 95% and 92% on average.

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Author information

Correspondence to Toshihiko Yamasaki.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Keywords

  • Feature Vector
  • High Precision
  • Quantum Information
  • Feature Point
  • Conventional Approach