In this paper a novel registration algorithm between 3D point clouds is presented. It exploits the fact that current 3D point descriptors (e.g., RoPS) are accompanied by local reference frames(LRF). LRFs of corresponding points are used to estimate the relative rotation between the point clouds. Thus, inlier matches will generate a cluster of rotation matrices. The size and shape of this cluster is unknown. We therefore develop a mean shift clustering algorithm for noisy rotation matrices. It finds the mode of the distribution to estimate the relative rotation. It is then used for estimating the translation vectors from the matched points. Here again mean shift is used for finding the translation component. The algorithm has been tested on different types of sources of 3D data (3D scanner, Lidar, and Structure from Motion(SfM)) of small scanned objects and urban scenes. In all these cases, the algorithm performed well outperforming state of the art algorithms in accuracy and in speed.
|Title of host publication||Proceedings - 2017 International Conference on 3D Vision, 3DV 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|State||Published - 25 May 2018|
|Event||7th IEEE International Conference on 3D Vision, 3DV 2017 - Qingdao, China|
Duration: 10 Oct 2017 → 12 Oct 2017
|Name||Proceedings - 2017 International Conference on 3D Vision, 3DV 2017|
|Conference||7th IEEE International Conference on 3D Vision, 3DV 2017|
|Period||10/10/17 → 12/10/17|
Bibliographical notePublisher Copyright:
© 2017 IEEE.
ASJC Scopus subject areas
- Media Technology
- Computer Vision and Pattern Recognition
- Signal Processing