Efficient discovery service for a digital library of 3D models

Many geographically distributed experts in different areas such as medical imaging, e-commerce, and digital museums, are in need of 3D models. Although 3D models are becoming widely available due to the recent technological advancement and modeling tools, we lack a digital library system where they can be searched and retrieved efficiently. In this paper, we focus on an efficient discovery service consisting of multilevel hierarchical browsing service that enables users to navigate large sets of 3D models. For this purpose, we use shape based clustering to abstract a large set of 3D models to a small set of representative models (key models). Our service applies clustering recursively to limit the number of key models that a user views at a time. Clustering is derived from metrics that are based on a concept of compression and similarity computation using surface signatures. Signatures are the two-dimensional representations of a 3D model and they can be used to define similarity between 3D models. We integrated the proposed browsing capability with 3DLIB, (a digital library for 3D models that we are building at Old Dominion University), and evaluated the proposed browsing service using the Princeton Shape Benchmark (PSB). Our evaluation shows significant better precision and recall as compared to other approaches.