Learning the Compositional Structure of Man-Made Objects for 3D Shape Retrieval

Abstract

While approaches based on local features play a more and more important role for 3D shape retrieval, the problems of feature selection and similarity measurement between sets of local features still remain open tasks. Common algorithms usually measure the similarity between two such sets by either establishing feature correspondences or by using Bag-of-Features (BoF) approaches. While establishing correspondences often involves a lot of manually chosen thresholds, BoF approaches can hardly model the spatial structure of the underlying 3D object. In this paper focusing on retrieval of 3D models representing man-made objects, we try to tackle both of these problems. Exploiting the fact that man-made objects usually consist of a small set of certain shape primitives, we propose a feature selection technique that decomposes 3D point clouds into sections that can be represented by a plane, a sphere, a cylinder, a cone, or a torus. We then introduce a probabilistic framework for analyzing and learning the spatial arrangement of the detected shape primitives with respect to training objects belonging to certain categories. The knowledge acquired in this learning process allows for efficient retrieval and classification of new 3D objects. We finally evaluate our algorithm on the recently introduced 3D Architecture Shape Benchmark, which mainly consists of 3D models representing man-made objects.