A distinctive binary descriptor and 2-point RANSACWC for point cloud registration

Point cloud registration is a fundamental problem in many applications. The point based on local shape descriptor has been widely researched. In order to further improve the performance of registration, novel method proposed this paper. First, binary designed establish correspondences between two clouds. high descriptiveness. Thus, more correct are established. Then, 3D transformation estimation technique developed, which multiple constraints used accelerate computation. When randomly selected do not satisfy constraints, iteration skipped. Finally, experiments performed analyze and technique. comparison with existing descriptors implemented three datasets. results demonstrate that our better matching performance. As for technique, combinations first analyzed. different presented best combination chose. comparative techniques can obtain accuracy computation efficiency.