A Novel Local Surface Description for Automatic 3D Object Recognition in Low Resolution Cluttered Scenes

Abstract

Local surface description is a critical stage for feature matching and recognition tasks. This paper presents a rotation invariant local surface descriptor, called 3D-Div. The proposed descriptor is based on the concept of 3D vector field's divergence, extensively used in electromagnetic theory. To generate a 3D-Div descriptor of a 3D surface, a local surface patch is parameterized around a randomly selected 3D point at a fixed scale. A unique Local Reference Frame (LRF) is then constructed at that 3D point using all the neighboring 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 descriptor is finally generated as the divergence of the reoriented 3D vector field. We tested our proposed descriptor on the challenging low resolution Washington RGB-D (Kinect) object dataset, for the task of automatic 3D object recognition. Reported experimental results show that 3D-Div based recognition achieves 93% accuracy as compared to 85% for existing state-of-the-art depth kernel descriptors [2].