3D-Div: A novel local surface descriptor for feature matching and pairwise range image registration

Abstract

This paper presents a novel local surface descriptor, called 3D-Div. The proposed descriptor is based on the concept of 3D vector fields divergence, extensively used in electromagnetic theory. To generate a 3D-Div descriptor of a 3D surface, a keypoint is first extracted on the 3D surface, then a local patch of a certain size is selected around that keypoint. A Local Reference Frame (LRF) is then constructed at the keypoint using all points forming the patch. A normalized 3D vector field is then computed at each point in the patch and referenced with LRF vectors. The 3D-Div descriptors are finally generated as the divergence of the reoriented 3D vector field. We tested our proposed descriptor on the low resolution Washington RGB-D (Kinect) object dataset. Performance was evaluated for the tasks of feature matching and pairwise range image registration. Experimental results showed that the proposed 3D-Div is 88% more computationally efficient and 47% more accurate than commonly used Spin Image (SI) descriptors.