A Robust Loss for Point Cloud Registration

• URL: http://arxiv.org/abs/2108.11682v1
• Date: Thu, 26 Aug 2021 09:56:47 GMT
• Title: A Robust Loss for Point Cloud Registration
• Authors: Zhi Deng, Yuxin Yao, Bailin Deng, Juyong Zhang
• Abstract summary: 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. 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.
• Score: 31.033915476145047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 outperforms the state-of-the-art optimization-based and unsupervised learning-based methods.

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