Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

Databases of 3D shapes have become widespread for a variety of applications, and a key research problem is searching these databases for similar shapes. This paper introduces a method for finding distinctive features of a shape that are useful for determining shape similarity. Although global shape descriptors have been developed to facilitate retrieval, they fail when local shape properties are the distinctive features of a class. Alternatively, local shape descriptors can be generated over the surface of shapes, but then storage and search of the descriptors becomes unnecessarily expensive, as perhaps only a few descriptors are sufficient to distinguish classes. The challenge is to select local descriptors from a query shape that are most distinctive for retrieval. Our approach is to define distinction as the retrieval performance of a local shape descriptor. During a training phase, we estimate descriptor likelihood using a multivariate Gaussian distribution of real-valued shape...

• Type: Conference
• Year: 2006
• Where: SMI
• Authors: Philip Shilane, Thomas A. Funkhouser
• Keywords: Global Shape Descriptors, Local Shape Descriptors, Shape Descriptors, SMI 2006