Autotagging to Improve Text Search for 3D Models

Corey Goldfeder; Peter K. Allen

Autotagging to Improve Text Search for 3D Models
Goldfeder, Corey
Allen, Peter K.
Technical reports
Computer Science
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Columbia University Computer Science Technical Reports
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Department of Computer Science, Columbia University
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New York
Text search on 3D models has traditionally worked poorly, as text annotations on 3D models are often unreliable or incomplete. In this paper we attempt to improve the recall of text search by automatically assigning appropriate tags to models. Our algorithm finds relevant tags by appealing to a large corpus of partially labeled example models, which does not have to be preclassified or otherwise prepared. For this purpose we use a copy of Google 3DWarehouse, a database of user contributed models which is publicly available on the Internet. Given a model to tag, we find geometrically similar models in the corpus, based on distances in a reduced dimensional space derived from Zernike descriptors. The labels of these neighbors are used as tag candidates for the model with probabilities proportional to the degree of geometric similarity. We show experimentally that text based search for 3D models using our computed tags can work as well as geometry based search. Finally, we demonstrate our 3D model search engine that uses this algorithm and discuss some implementation issues.
Computer science
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Suggested Citation:
Corey Goldfeder, Peter K. Allen, , Autotagging to Improve Text Search for 3D Models, Columbia University Academic Commons, .

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