Dual spin-image: A bi-directional spin-image variant using multi-scale radii for 3D local shape description

Obtaining good feature descriptors for 3D local shape under different conditions, such as noise, clutter, occlusion, and limited overlap, is still a challenging task in computer vision. In this paper, we present a variant of spin-image called dual spin-image (Dual-SI) for 3D local shape description that encodes bi-directional information between an oriented point and its surrounding neighbors within a given radius. By accumulating the parameters that encode the 3D point information into a 2D space bidirectionally, we can generate two different signatures. These signatures are then concatenated to form the Dual-SI feature descriptor. Finally, we propose a multi-scale radius approach to address the problem of occlusion and make use of a weighted kernel to address the noise problem. We tested our Dual-SI feature descriptor on popular datasets addressing 3D object registration, 3D object recognition, and shape retrieval scenarios. We also conduct experiments for 3D point cloud registration to further evaluate the effectiveness of our method. Consensus experimental results show that our Dual-SI achieves outstanding performance on datasets with various nuisances and application contexts.