MGCN Descriptor Learning using Multiscale GCNs

Yiqun Wang, Jing Ren, Dong Ming Yan, Jianwei Guo, Xiaopeng Zhang, Peter Wonka

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

We propose a novel framework for computing descriptors for characterizing points on three-dimensional surfaces. First, we present a new non-learned feature that uses graph wavelets to decompose the Dirichlet energy on a surface. We call this new feature Wavelet Energy Decomposition Signature (WEDS). Second, we propose a new Multiscale Graph Convolutional Network (MGCN) to transform a non-learned feature to a more discriminative descriptor. Our results show that the new descriptor WEDS is more discriminative than the current state-of-the-art non-learned descriptors and that the combination of WEDS and MGCN is better than the state-of-the-art learned descriptors. An important design criterion for our descriptor is the robustness to different surface discretizations including triangulations with varying numbers of vertices. Our results demonstrate that previous graph convolutional networks significantly overfit to a particular resolution or even a particular triangulation, but MGCN generalizes well to different surface discretizations. In addition, MGCN is compatible with previous descriptors and it can also be used to improve the performance of other descriptors, such as the heat kernel signature, the wave kernel signature, or the local point signature.
Original languageEnglish (US)
JournalACM Transactions on Graphics
Volume39
Issue number4
DOIs
StatePublished - Aug 12 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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