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We present a fast feature-metric point cloud registration framework, whichenforces the optimisation of registration by minimising a feature-metricprojection error without correspondences. The advantage of the feature-metricprojection error is robust to noise, outliers and density difference incontrast to the geometric projection error. Besides, minimising thefeature-metric projection error does not need to search the correspondences sothat the optimisation speed is fast. The principle behind the proposed methodis that the feature difference is smallest if point clouds are aligned verywell. We train the proposed method in a semi-supervised or unsupervisedapproach, which requires limited or no registration label data. Experimentsdemonstrate our method obtains higher accuracy and robustness than thestate-of-the-art methods. Besides, experimental results show that the proposedmethod can handle significant noise and density difference, and solve bothsame-source and cross-source point cloud registration.

Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences