Optimal Transport on the Lie Group of Roto-Translations

The roto-translation group SE(2) has been of active interest in image analysis due to methods that lift the image data to multi-orientation representations defined in this Lie group. This has led to impactful applications of crossing-preserving flows for image de-noising, geodesic tracking, and roto-translation equivariant deep learning. In this talk, I will enumerate a computational framework for optimal transportation over Lie groups, with a special focus on SE(2). I will describe several theoretical aspects such as the non-optimality of group actions as transport maps, invariance and equivariance of optimal transport, and the quality of the entropic-regularized optimal transport plan using geodesic distance approximations. Finally, I will illustrate a Sinkhorn-like algorithm that can be efficiently implemented using fast and accurate distance approximations of the Lie group and GPU-friendly group convolutions. We report advancements with the experiments on 1) 2D shape/ image barycenters, 2) interpolation of planar orientation fields, and 3) Wasserstein gradient flows on SE(2). We observe that our framework of lifting images to SE(2) and optimal transport with left-invariant anisotropic metrics leads to equivariant transport along dominant contours and salient line structures in the image and leads to meaningful interpolations compared to their counterparts on R^2. *Joint work with Daan Bon, Gijs Bellaard, Olga Mula, and Remco Duits from CASA – TU/e. Preprint: https://arxiv.org/abs/2402.15322 (to appear in SIAM Journal in Imaging Sciences 2025)