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- W3204147346 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 outperform the state-of-the-art optimization-based and unsupervised learning-based methods." @default.
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- W3204147346 date "2021-08-26" @default.
- W3204147346 modified "2023-09-23" @default.
- W3204147346 title "A Robust Loss for Point Cloud Registration" @default.
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