3D Shape Matching with 3D Shape Contexts

Abstract

Content based 3D shape retrieval for broad domains like the World Wide Web has recently gained considerable attention in Computer Graphics community. One of the main challenges in this context is the mapping of 3D objects into compact canonical representations referred to as descriptors or feature vector, which serve as search keys during the retrieval process. The descriptors should have certain desirable properties like invariance under scaling, rotation and translation as well as a descriptive power providing a basis for similarity measure between threedimensional objects which is close to the human notion of resemblance. In this paper we introduce an enhanced 3D approach of the recently introduced 2D Shape Contexts that can be used for measuring 3d shape similarity as fast, intuitive and powerful similarity model for 3D objects. The Shape Context at a point captures the distribution over relative positions of other shape points and thus summarizes global shape in a rich, local descriptor. Shape Contexts greatly simplify recovery of correspondences between points of two given shapes. Moreover, the Shape Context leads to a robust score for measuring shape similarity, once shapes are aligned.