Scalable Object Classification Using Range Images

Abstract

We present a novel scalable framework for free-form object classification in range images. The framework includes an automatic 3D object recognition system in range images and a scalable database structure to learn new instances and new categories efficiently. We adopt the TAX model, previously proposed for un-supervised object modeling in 2D images, to construct our hierarchical model of object classes from unlabelled range images. The hierarchical model embodies unorganized shape patterns of 3D objects in various classes in a tree structure with probabilistic distributions. A new visual vocabulary is introduced to represent a range image as a set of visual words for the process of hierarchical model inference, classification and online learning. We also propose an online learning algorithm that updates the hierarchical model efficiently thanks to the tree structure, when a new object should be learned into the model. Extensive experiments demonstrate average classification rates of 94% on a large synthetic dataset (1,350 training images and 450 test images for 9 object classes) and 88.4% on 1,433 depth images captured from real-time range sensors. We also show that our approach outperforms the original TAX method in terms of recall rate and stability.