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AWEDD: a descriptor simultaneously encoding multiscale extrinsic and intrinsic shape features

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Abstract

We construct a novel descriptor called anisotropic wavelet energy decomposition descriptor (AWEDD) for non-rigid shape analysis, based on anisotropic diffusion geometry. We first extend the Dirichlet energy of the vertex coordinate function to an anisotropic version, then use multiscale anisotropic spectral manifold wavelets to decompose the Dirichlet energy to all vertices and collect local energy at each vertex to form AWEDD. AWEDD simultaneously encodes multiscale extrinsic and intrinsic shape features, which are more informative and robust than purely intrinsic or extrinsic descriptors. And the introduction of anisotropy endows AWEDD with stronger abilities of feature discrimination and intrinsic symmetry identification. Our results demonstrate that AWEDD is more discriminative than current state-of-the-art descriptors. In addition, we show that AWEDD is an excellent choice of the initial inputs for various shape analysis approaches, such as functional map pipelines and deep convolutional architectures.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Nos. 62172447, 61876191), Hunan Provincial Natural Science Foundation of China (No. 2021JJ30172, 2023JJ40769), and the Open Project Program of the State Key Laboratory of Multimodal Artificial Intelligence Systems (No. 202200025).

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  1. Institute of Engineering Modeling and Scientific Computing, Central South University, Changsha, China

    Shengjun Liu, Feifan Luo, Qinsong Li & Xinru Liu

  2. School of Mathematics and Statistics, Hunan First Normal University, Changsha, China

    Ling Hu

  3. Big Data Institute, Central South University, Changsha, China

    Qinsong Li

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  1. Shengjun Liu
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Liu, S., Luo, F., Li, Q. et al. AWEDD: a descriptor simultaneously encoding multiscale extrinsic and intrinsic shape features. Vis Comput 40, 2537–2554 (2024). https://doi.org/10.1007/s00371-023-02935-6

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