Recurrent Multi-view Alignment Network for Unsupervised Surface Registration

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Wanquan Feng
  • Juyong Zhang
  • Hongrui Cai
  • Haofei Xu
  • Hujun Bao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021
Pages10297-10307
Publication statusPublished - Jun 2021

Conference

Title2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)
LocationVirtual
Period19 - 25 June 2021

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.

Research Area(s)

  • cs.CV, cs.GR

Citation Format(s)

Recurrent Multi-view Alignment Network for Unsupervised Surface Registration. / Feng, Wanquan; Zhang, Juyong; Cai, Hongrui; Xu, Haofei; Hou, Junhui; Bao, Hujun.

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021. 2021. p. 10297-10307.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review