A Search Engine for 3D Models

increasing need for a search engine to help people find them
(e.g., a Google for 3D models). Unfortunately, traditional
text-based search techniques are not always effective for 3D data.
In this talk, we investigate new shape-based search methods.
A key challenge is to find a computational representation of shape
(a "shape descriptor") that is concise, robust, quick to compute,
efficient to match, and discriminating between similar and dissimilar
shapes.

In this talk, I will describe shape descriptors designed for computer
graphics models commonly found on the Web (i.e., they may contain
arbitrary degeneracies and alignments). We have experimented with
them in a Web-based search engine that allows users to query for
3D models based on similarities to 3D sketches, 3D models, 2D sketches,
and/or text keywords. We find our best shape matching methods provide
better precision-recall performance than related approaches
and are fast enough to return query results from a repository of
20,000 polygonal models in under a second. You can try them out at:
http://shape.cs.princeton.edu.

Joint work with Patrick Min, Michael Kazhdan, Robert Osada, Phil Shilane,
Joyce Chen, Alex Halderman, David Dobkin, and David Jacobs.