Abstract
Point cloud alignment is a key technique in the field of computer vision, which involves estimating the transformation between two-point clouds. With the development of optimization methods and deep learning techniques, the robustness and efficiency of point cloud alignment have been significantly improved. Recent studies have combined these two methods to further optimize the performance. Meanwhile, advances in 3D sensing and 3D reconstruction techniques have given rise to the new research field of cross-source point cloud alignment. This paper reviews recent advances in point cloud alignment, including both homologous and cross-source alignment techniques, and explores the interconnection of optimization and deep learning techniques. In addition, the paper reviews relevant benchmark datasets and explores their applications in different domains. Finally, future research directions for point cloud alignment are also described.
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