3D content-based retrieval with spin images

Abstract

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; the establishment of open standards for 3D data interchange. The ever increasing availability of 3D models demands tools to support their effective and efficient management. Among these tools, those enabling content-based retrieval play a key role. We present a novel approach to 3D content-based retrieval 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; 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. These results indicate that spin images can be successfully exploited for content-based retrieval of 3D objects