VOID: 3D object recognition based on voxelization in invariant distance space

Recognizing 3D objects based on local feature descriptors, in point cloud scenes with occlusion and clutter, is a very challenging task. Most existing 3D local feature descriptors rely on normal information to encode local features, however, they ignore the normal-sign-ambiguity issue, which greatly limits their descriptiveness and robustness. This paper proposes a method called VOxelization in Invariant Distance space for 3D object recognition. First, we propose a VOID descriptor that is invariant to normal-sign-ambiguity, and is also rotation-invariant, distinctive, robust, and efficient. Second, a VOID-based 3D object recognition method considering the self-similarity between local features is proposed to enhance the recognition performance. Five standard datasets are employed to validate our proposed method as well as comparison with the state-of-the-arts. The results suggest that: (1) VOID descriptor is invariant to normal-sign-ambiguity, distinctive, and robust; (2) VOID-based 3D object recognition achieves outstanding recognition performance, i.e., 99.47%, 93.07% and 99.18%, on the U3OR, Queen’s and Ca’ Foscari Venezia datasets, respectively.