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Cited by 3,412

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Surface simplification using quadric error metrics

Garland, Michael, Heckbert, Paul S.·1997·Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97

Abstract

Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplification algorithm which can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm is able to join unconnected regions of models. This can facilitate much better approximations, both visually and with re-spect to geometric error. In order to allow topological joining, our system also supports non-manifold surface models.

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Metadata

Authors
Garland, M., & Heckbert, P. S.
Venue
Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97
Publisher
ACM Press
Published
1997
Pages
209-216
OpenAlex
W2235901111
Cited by
3,412
References
6
FWCI
34.62
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Surface simplification using quadric error metrics. https://app.cochat.ai/papers/10.1145/258734.258849
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Garland, M., & Heckbert, P. S. (1997). Surface simplification using quadric error metrics. Proceedings of the 24th annual conference on Computer graphics and interactive techniques - SIGGRAPH '97. https://doi.org/10.1145/258734.258849

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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.