Point cloud registration in military scenarios is pivotal to automatic object reconstruction and recognition. This paper proposes 1) a multi-scale binary feature representation called mLoVS (multi-scale local voxelized structure) and 2) a “min-pooling” based feature matching technique for accurate registration of tank point clouds. The key insight of our method is that traditional fixed-scale feature matching methods either suffer from limited shape information or data missing caused by occlusion, while the multi-scale way provides a flexible matching choice. In addition, the binary nature of our feature representation can alleviate the increased time budget required by multi-scale feature matching. Experiments on several sets of tank point clouds confirm the effectiveness and overall superiority of our method.

Date Published: 31 July 2019
PDF: 5 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980L (31 July 2019); doi: 10.1117/12.2540407

Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)