Robust consensus-aware network for 3D point registration

2022 
Outlier correspondence removal is an important task for feature-based point cloud registration. Given putative correspondences contaminated by outliers between two overlapped scans, we propose a Robust Consensus-Aware Network, which labels the correspondences as inliers or outliers and predicts the rigid transformation to align the point clouds. The proposed method dedicates to mining the global consensus of correct correspondences (inliers). So it can learn distinctive features for each correspondence. Specifically, the proposed network comprises three novel operations. First, by capturing the global consensus information in an attentive manner, the network projects the input correspondences into a
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