Unlocking Geodesic Gromov-Wasserstein Distances for 3D Modeling

arXiv:2609.32824 · cs.CV, cs.DS, eess.IV · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.DS, eess.IV

Submitted: 2026-09-26

Updated: 2026-10-02

Terminology

• Optimal Transport — 16× in this paper · explained in 5 episodes
• Wasserstein Distance — 16× in this paper · explained in 4 episodes
• Gromov-Wasserstein Distance — 13× in this paper
• Accuracy — 6× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Calibration — 3× in this paper · explained in 7 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Fused Gromov-Wasserstein Distance — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Angular Error — 1× in this paper · explained in 1 episode
• Generative AI — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Near-Linear Time Generalized Sinkhorn Algorithms for Bounded Genus Graphs
• A Survey on Algorithmic Developments in Optimal Transport Problem with Applications
• Manifold Random Features
• Optimal Transport for Machine Learners
• Semi-relaxed Gromov-Wasserstein divergence with applications on graphs
• Sinkhorn Algorithm for Sequentially Composed Optimal Transports
• Thingi10K: A Dataset of 10,000 3D-Printing Models

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