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Cited by 502

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Fast exact and approximate geodesics on meshes

Open access
Surazhsky, Vitaly, Surazhsky, Tatiana, Kirsanov, Danil, Gortler, Steven J., Hoppe, Hugues·2005·ACM Transactions on Graphics

Abstract

The computation of geodesic paths and distances on triangle meshes is a common operation in many computer graphics applications. We present several practical algorithms for computing such geodesics from a source point to one or all other points efficiently. First, we describe an implementation of the exact "single source, all destination" algorithm presented by Mitchell, Mount, and Papadimitriou (MMP). We show that the algorithm runs much faster in practice than suggested by worst case analysis. Next, we extend the algorithm with a merging operation to obtain computationally efficient and accurate approximations with bounded error. Finally, to compute the shortest path between two given points, we use a lower-bound property of our approximate geodesic algorithm to efficiently prune the frontier of the MMP algorithm. thereby obtaining an exact solution even more quickly.

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Metadata

Authors
Surazhsky, V., Surazhsky, T., Kirsanov, D., Gortler, S. J., & Hoppe, H.
Venue
ACM Transactions on Graphics
Published
2005-07
Volume
24
Issue
3
Pages
553-560
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W2161253909
Cited by
502
References
21
FWCI
36.37
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Fast exact and approximate geodesics on meshes. https://app.cochat.ai/papers/10.1145/1073204.1073228
The paper
Surazhsky, V., Surazhsky, T., Kirsanov, D., Gortler, S. J., & Hoppe, H. (2005). Fast exact and approximate geodesics on meshes. ACM Transactions on Graphics. https://doi.org/10.1145/1073204.1073228

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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.