PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

• URL: http://arxiv.org/abs/2103.05465v1
• Date: Tue, 9 Mar 2021 14:56:08 GMT
• Title: PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
• Authors: Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, Chiew-Lan Tai
• Abstract summary: We present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. Our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets.
• Score: 38.93610732090426
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning methods in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand-crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors.

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