Abstract Point cloud registration focuses on exploring and exploiting feature similarities to match multiple point clouds of a scene from different viewpoints. However, in many real-world scenarios, points are missing (e.g., multiple LiDAR scans), or there is a substantial variation of points in different viewpoints (e.g., from 3D reconstruction), making registration more difficult. In this paper, we argue that the quality of point clouds should be improved to ease the matching process.
