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Learning Spectral Transform Network on 3D Surface for Non-rigid Shape Analysis

  • Ruixuan Yu
  • Jian SunEmail author
  • Huibin Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

Designing a network on 3D surface for non-rigid shape analysis is a challenging task. In this work, we propose a novel spectral transform network on 3D surface to learn shape descriptors. The proposed network architecture consists of four stages: raw descriptor extraction, surface second-order pooling, mixture of power function-based spectral transform, and metric learning. The proposed network is simple and shallow. Quantitative experiments on challenging benchmarks show its effectiveness for non-rigid shape retrieval and classification, e.g., it achieved the highest accuracies on SHREC’14, 15 datasets as well as the “range” subset of SHREC’17 dataset.

Keywords

Non-rigid shape analysis Spectral transform Shape representation 

Notes

Acknowledgement

This work is supported by National Natural Science Foundation of China under Grants 11622106, 61711530242, 61472313, 11690011, 61721002.

Supplementary material

478822_1_En_28_MOESM1_ESM.pdf (39 kb)
Supplementary material 1 (pdf 39 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Xi’an Jiaotong UniversityXi’anChina

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