Robust consensus-aware network for 3D point registration

Abstract

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 discriminative feature space. Next, we exploit the feature similarity among correspondences to establish interactions within inlier or outlier correspondences, and aggregate the features of correct correspondences for outlier removal. Finally, we recover the rigid transformation by mining multi-level context with a motion estimation module. Extensive experiments on real-world datasets demonstrate that our approach achieves high registration accuracy and efficiency.