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In this paper, we propose a coarse-to-fine integration solution inspired bythe classical ICP algorithm, to pairwise 3D point cloud registration with twoimprovements of hybrid metric spaces (eg, BSC feature and Euclidean geometryspaces) and globally optimal correspondences matching. First, we detect thekeypoints of point clouds and use the Binary Shape Context (BSC) descriptor toencode their local features. Then, we formulate the correspondence matchingtask as an energy function, which models the global similarity of keypoints onthe hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate theglobally optimal correspondences through optimizing the energy function by theKuhn-Munkres algorithm and then calculate the transformation based on thecorrespondences. Finally,we iteratively refine the transformation between twopoint clouds by conducting optimal correspondences matching and transformationcalculation in a mutually reinforcing manner, to achieve the coarse-to-fineregistration under an unified framework.The proposed method is evaluated andcompared to several state-of-the-art methods on selected challenging datasetswith repetitive, symmetric and incomplete structures.Comprehensive experimentsdemonstrate that the proposed IGSP algorithm obtains good performance andoutperforms the state-of-the-art methods in terms of both rotation andtranslation errors.

Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration