Voxelized Shape and Color Histograms for RGB-D

Real world environments typically include objects with different perceptual appearances. A household, for example, includes textured, textureless and even partially transparent objects. While perception methods exist that work well on one such class of objects at a time, the perception of various classes of objects in a scene is still a challenge. It is our view that the construction of a descriptor that takes both color and shape into account, thereby fostering high discriminating power, will help to solve this problem. In this paper we present an approach that is capable of efficiently capturing both the geometric and visual appearance of common objects of daily use into one feature. We showcase this feature’s applicability for the purpose of classifying objects in cluttered scenes with obstructions, and we evaluate two classification approaches. In our experiments we make use of Kinect, a new RGB-D device, and build a database of 63 objects. Preliminary results on novel views show recognition rates of 72.2%.