Object Categorization from Range Images Using a Hierarchical Compositional Representation

This paper proposes a novel hierarchical compositional representation of 3D shape that can accommodate a large number of object categories and enables efficient learning and inference. The hierarchy starts with simple pre-defined parts on the first layer, after which subsequent layers are learned recursively by taking the most statistically significant compositions of parts from the previous layer. Our representation is able to scale because of its very economical use of memory and because subparts of the representation are shared. We apply our representation to 3D multi-class object categorization. Object categories are represented by histograms of compositional parts, which are then used as inputs to an SVM classifier. We present results for two datasets, Aim Shape [1] and the Washington RGB-D Object Dataset [2], and demonstrate the competitive performance of our method.