Spin Images for Retrieval of 3D Objects by Local and Global Similarity

The ever increasing availability of 3D models demands for tools supporting their effective and efficient management. Among these tools, those enabling content-based retrieval play a key role. In this paper we present a novel approach to global and local content-based retrieval of 3D objects that is based on spin images. Spin images are used to derive a view-independent description of both database and query objects. A set of spin images is first created for each object and the parts it is composed of; then, a descriptor is evaluated for each spin image in the set; clustering is performed on the set of image-based descriptors of each object to achieve a compact representation. Experimental results are presented for a test database of about 300 models, showing the effectiveness of retrieval for both object and part similarity.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Alberto Del Bimbo, Jürgen Assfalg, Pietro Pala
• Keywords: Computer Vision, ICPR 2004, Image-based Descriptors, Local Content-based Retrieval, Spin Images