Multi-scale binary geometric feature description and matching for accurate registration of point clouds

Abstract

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.