From 3D Point Clouds to Pose-Normalised Depth Maps
International Journal of Computer Vision
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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.