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Multi-Scale Salient Features for Analyzing 3D Shapes

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Abstract

Extracting feature regions on mesh models is crucial for shape analysis and understanding. It can be widely used for various 3D content-based applications in graphics and geometry field. In this paper, we present a new algorithm of extracting multi-scale salient features on meshes. This is based on robust estimation of curvature on multiple scales. The coincidence between salient feature and the scale of interest can be established straightforwardly, where detailed feature appears on small scale and feature with more global shape information shows up on large scale. We demonstrate this kind of multi-scale description of features accords with human perception and can be further used for several applications as feature classification and viewpoint selection. Experiments exhibit that our method as a multi-scale analysis tool is very helpful for studying 3D shapes.

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Authors and Affiliations

  1. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Yong-Liang Yang

  2. Tsinghua National Lab for Informatics Science & Technology, Tsinghua University, Beijing, 100084, China

    Yong-Liang Yang & Chao-Hui Shen

Authors
  1. Yong-Liang Yang
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  2. Chao-Hui Shen
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Corresponding author

Correspondence to Yong-Liang Yang.

Additional information

This work was supported by the National Basic Research 973 Program of China under Grant No. 2011CB302203 the National Natural Science Foundation of China under Grant No. 61120106007 and the National High Technology Research and Development 863 Program of China under Grant No. 2012AA011801.

*The preliminary version of the paper was published in the Proceedings of the 2012 Computational Visual Media Conference.

**The test models used in this paper are courtesy of the Stanford 3D scanning repository and the AIM@SHAPE shape repository.

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Yang, YL., Shen, CH. Multi-Scale Salient Features for Analyzing 3D Shapes. J. Comput. Sci. Technol. 27, 1092–1099 (2012). https://doi.org/10.1007/s11390-012-1287-z

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  • DOI: https://doi.org/10.1007/s11390-012-1287-z

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