Retrieval of 3D objects by visual similarity

Along with images and videos, 3D models have recently gained increasing attention for a number of reasons: advancements in 3D hardware and software technologies, their ever decreasing prices and increasing availability, affordable 3D authoring tools, and the establishment of open standards for 3D data interchange. 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 method to use spin images for re-trieval by content of 3D objects based on global object features. 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; 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 of the object, thus allowing for efficient indexing and matching. Experimental results are presented for a test database of about 300 models, showing the effectiveness of retrieval by object similarity