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Cited by 935

The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.

A search engine for 3D models

Funkhouser, Thomas, Min, Patrick, Kazhdan, Michael, Chen, Joyce, Halderman, Alex, Dobkin, David, Jacobs, David·2003·ACM Transactions on Graphics

Abstract

As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245% better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).

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Metadata

Authors
Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., & Jacobs, D.
Venue
ACM Transactions on Graphics
Published
2003-01
Volume
22
Issue
1
Pages
83-105
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W2075597533
Cited by
935
References
69
FWCI
82.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A search engine for 3D models. https://app.cochat.ai/papers/10.1145/588272.588279
The paper
Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., & Jacobs, D. (2003). A search engine for 3D models. ACM Transactions on Graphics. https://doi.org/10.1145/588272.588279

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.