Rotational contour signatures for both real-valued and binary feature representations of 3D local shape

This paper presents a rotational contour signatures (RCS) method for both real-valued and binary descriptions of 3D local shape. RCS comprises several signatures that characterize the 2D contour information derived from 3D-to-2D projection of the local point cloud. The inspiration of our encoding technique comes from that when viewing towards an object, its contour is an effective and robust cue for representing its shape. In order to achieve a comprehensive geometry encoding, the local surface is continually rotated in a predefined local reference frame (LRF) so that multi-view information is obtained. A peculiar trait of our RCS method is its seamless extension to binary representations to accelerate feature matching and reduce storage consumption. Specifically, we resort to three techniques, i.e., thresholding, quantization and geometrical binary encoding, to generate RCS binary strings. In contrast to 2D image area, there are quite rare 3D binary descriptors yet in 3D computer vision. We deploy experiments on three standard datasets including shape retrieval, 3D object recognition and 2.5D point cloud view matching scenarios with a rigorous comparison with six state-of-the-art descriptors. The comparative outcomes confirm numerous merits of our RCS method, e.g., highly discriminative, compact, computational efficient and robust to many nuisances including noise, mesh resolution variation, clutter and occlusion. We also show the versatility of RCS in matching of both LiDAR and Kinect point clouds.