Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

Wanquan Feng, Juyong Zhang*, Hongrui Cai, Haofei Xu, Junhui Hou, Hujun Bao

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

39 Citations (Scopus)

Abstract

Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data. In this paper, we resolve these two challenges simultaneously. First, we propose to represent the non-rigid transformation with a point-wise combination of several rigid transformations. This representation not only makes the solution space well-constrained but also enables our method to be solved iteratively with a recurrent framework, which greatly reduces the difficulty of learning. Second, we introduce a differentiable loss function that measures the 3D shape similarity on the projected multi-view 2D depth images so that our full framework can be trained end-to-end without ground truth supervision. Extensive experiments on several different datasets demonstrate that our proposed method outperforms the previous state-of-the-art by a large margin. The source codes are available at https://github.com/WanquanF/RMA-Net.
Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Subtitle of host publicationCVPR 2021
PublisherIEEE
Pages10292-10302
ISBN (Electronic)9781665445092
ISBN (Print)9781665445108
DOIs
Publication statusPublished - 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) - Virtual
Duration: 19 Jun 202125 Jun 2021
http://cvpr2021.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings
https://openaccess.thecvf.com/CVPR2021

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)
Abbreviated titleCVPR'21
Period19/06/2125/06/21
Internet address

Bibliographical note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Research Keywords

  • cs.CV
  • cs.GR

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