Anisotropic Spectral Manifold Wavelet Descriptor for Deformable Shape Analysis and Matching

Li, Qinsong; Liu, Shengjun; Hu, Ling; Liu, Xinru

Anisotropic Spectral Manifold Wavelet Descriptor for Deformable Shape Analysis and Matching

Files

041-044.pdf (2.02 MB)

Date

2018

Authors

Li, Qinsong
Liu, Shengjun
Hu, Ling
Liu, Xinru

Publisher

The Eurographics Association

Abstract

In this paper, we present a novel framework termed Anisotropic Spectral Manifold Wavelet Transform (ASMWT) for shape analysis. ASMWT comprehensively analyzes the signals from multiple directions on local manifold regions of the shape with a series of low-pass and band-pass frequency filters in each direction. Using the ASMWT coefficients of a very simple function, we efficiently construct a localizable and discriminative multiscale point descriptor, named as the Anisotropic Spectral Manifold Wavelet Descriptor (ASMWD). Since the filters used in our descriptor are direction-sensitive and able to robustly reconstruct the signals with a finite number of scales, it makes our descriptor be intrinsic-symmetry unambiguous, compact as well as efficient. The extensive experimental results demonstrate that our method achieves significant performance than several state-of-the-art methods when applied in vertex-wise shape matching.

        @inproceedings{10.2312:pg.20181276
,
booktitle = {Pacific Graphics Short Papers
},
editor = {Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
},
title = {{Anisotropic Spectral Manifold Wavelet Descriptor for Deformable Shape Analysis and Matching
}},
author = {Li, Qinsong and 
Liu, Shengjun and 
Hu, Ling and 
Liu, Xinru
},
year = {2018
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-073-4
},
DOI = {10.2312/pg.20181276
}
}