In this paper, we propose a coarse-to-fine integration solution inspired by
the classical ICP algorithm, to pairwise 3D point cloud registration with two
improvements of hybrid metric spaces (eg, BSC feature and Euclidean geometry
spaces) and globally optimal correspondences matching. First, we detect the
keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to
encode their local features. Then, we formulate the correspondence matching
task as an energy function, which models the global similarity of keypoints on
the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the
globally optimal correspondences through optimizing the energy function by the
Kuhn-Munkres algorithm and then calculate the transformation based on the
correspondences. Finally,we iteratively refine the transformation between two
point clouds by conducting optimal correspondences matching and transformation
calculation in a mutually reinforcing manner, to achieve the coarse-to-fine
registration under an unified framework.The proposed method is evaluated and
compared to several state-of-the-art methods on selected challenging datasets
with repetitive, symmetric and incomplete structures.Comprehensive experiments
demonstrate that the proposed IGSP algorithm obtains good performance and
outperforms the state-of-the-art methods in terms of both rotation and
translation errors.

该研究提出了一种新的方法，使用混合度量空间（例如 BSC 特征和欧几里德几何空间）和全局最优对应匹配，通过粗到细的迭代优化对点云进行配准，并在多个测试数据集上取得了好的性能表现。