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 language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Subtitle of host publication | CVPR 2021 |
Publisher | IEEE |
Pages | 10292-10302 |
ISBN (Electronic) | 9781665445092 |
ISBN (Print) | 9781665445108 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) - Virtual Duration: 19 Jun 2021 → 25 Jun 2021 http://cvpr2021.thecvf.com/ https://ieeexplore.ieee.org/xpl/conhome/1000147/all-proceedings https://openaccess.thecvf.com/CVPR2021 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 2575-7075 |
Conference
Conference | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) |
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Abbreviated title | CVPR'21 |
Period | 19/06/21 → 25/06/21 |
Internet address |
Bibliographical note
Research Unit(s) information for this publication is provided by the author(s) concerned.Research Keywords
- cs.CV
- cs.GR