Unlocking Geodesic Gromov-Wasserstein Distances for 3D Modeling
\textit{Gromov-Wasserstein Distances} (GWDs) provide quantitative ways of comparing probabilistic distributions defined on different metric spaces by applying techniques from the optimal transport theory. As such, GWD can be potentially useful in a large variety of applications ranging from graph matching problems to 3D object detection. However its practical use at scale is significantly limited by cubic time complexity computations involving dense intra-space distance matrices. Even though in the Euclidean metric spaces several techniques (e.g. involving scalable kernel methods) were proposed to address it, to the best of our knowledge, analogous techniques for general geodesic distance...
| Authors | Krzysztof Marcin Choromanski, Derek Long, Ananya Parashar et al. |
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| Primary category | cs.CV |
| Published | 26 September 2026 |
| Last updated at source | 2 October 2026 |
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