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Cited by 481

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Convolutional wasserstein distances: efficient optimal transportation on geometric domains

Open access
Solomon, Justin, de Goes, Fernando, Peyré, Gabriel, Cuturi, Marco, Butscher, Adrian, Nguyen, Andy, Du, Tao, Guibas, Leonidas·2015·ACM Transactions on Graphics

Abstract

This paper introduces a new class of algorithms for optimization problems involving optimal transportation over geometric domains. Our main contribution is to show that optimal transportation can be made tractable over large domains used in graphics, such as images and triangle meshes, improving performance by orders of magnitude compared to previous work. To this end, we approximate optimal transportation distances using entropic regularization. The resulting objective contains a geodesic distance-based kernel that can be approximated with the heat kernel. This approach leads to simple iterative numerical schemes with linear convergence, in which each iteration only requires Gaussian convolution or the solution of a sparse, pre-factored linear system. We demonstrate the versatility and efficiency of our method on tasks including reflectance interpolation, color transfer, and geometry processing.

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Metadata

Authors
Solomon, J., de Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., & Guibas, L.
Venue
ACM Transactions on Graphics
Published
2015-07-27
Volume
34
Issue
4
Pages
1-11
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W2009172320
Cited by
481
References
36
FWCI
63.73
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Convolutional wasserstein distances: efficient optimal transportation on geometric domains. https://app.cochat.ai/papers/10.1145/2766963
The paper
Solomon, J., de Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A., Du, T., & Guibas, L. (2015). Convolutional wasserstein distances: efficient optimal transportation on geometric domains. ACM Transactions on Graphics. https://doi.org/10.1145/2766963

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