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A robust loss for point cloud registration

Deng, Zhi ORCID:, Yao, Yuxin, Deng, Bailin ORCID: and Zhang, Juyong 2021. A robust loss for point cloud registration. Presented at: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Virtual, 11-17 October 2021. 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

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The performance of surface registration relies heavily on the metric used for the alignment error between the source and target shapes. Traditionally, such a metric is based on the point-to-point or point-to-plane distance from the points on the source surface to their closest points on the target surface, which is susceptible to failure due to instability of the closest-point correspondence. In this paper, we propose a novel metric based on the intersection points between the two shapes and a random straight line, which does not assume a specific correspondence. We verify the effectiveness of this metric by extensive experiments, including its direct optimization for a single registration problem as well as unsupervised learning for a set of registration problems. The results demonstrate that the algorithms utilizing our proposed metric outperform the state-of-the-art optimization-based and unsupervised learning-based methods.

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Date of First Compliant Deposit: 17 September 2021
Date of Acceptance: 22 July 2021
Last Modified: 09 Nov 2022 11:32

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