Li, QinsongLiu, ShengjunHu, LingLiu, XinruFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes2018-10-072018-10-072018978-3-03868-073-4https://doi.org/10.2312/pg.20181276https://diglib.eg.org:443/handle/10.2312/pg20181276In 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.10.2312/pg.2018127641-44