Point Cloud Matching Based on 3D Self-Similarity
Point cloud is one of the primitive representations of 3D data nowadays. Despite that much work has been done in 2D image matching, matching 3D points achieved from different perspective or at different time remains to be a challenging problem. This paper proposes a 3D local descriptor based on 3D self-similarities. The authors not only extend the concept of 2D self-similarity to the 3D space, but also establish the similarity measurement based on the combination of geometric and photometric information. The matching process is fully automatic i.e. needs no manually selected land marks.