Improving Lunar Topography with Deep Learning Schr"odinger Bridges

arXiv:2606.14638 · cs.CV, astro-ph.EP · Submitted 2026-06-12 · Read on arXiv

Matthew Repasky, Erwan Mazarico, Michael K. Barker, Stefano Bertone, Terence J. Sabaka, Yao Xie

cs.CV, astro-ph.EP

Submitted: 2026-06-12

Journal ref: The Planetary Science Journal 7.6 (2026): 139

DOI: 10.3847/PSJ/ae6244

Code: https://github.com/mrepasky3/topo_sr_schrodinger

License: http://creativecommons.org/licenses/by/4.0/

The gist: Increasing the resolution of planetary topography models can enable a better understanding of surface processes and geomorphology; however, existing analytical super-resolution methods are expensive

Terminology

Abstract

Increasing the resolution of planetary topography models can enable a better understanding of surface processes and geomorphology; however, existing analytical super-resolution methods are expensive and difficult to apply at large scales. Generative models provide the tools to learn complex relationships within data and can be applied at scale due to hardware accelerators and parallelization. We present a diffusion-based Schr"odinger Bridge (SB) generative modeling approach for lunar topography super-resolution, connecting the distribution of low-resolution topography to that of high-resolution topography, incorporating physically-constraining optical imagery. Our approach is inspired by existing Shape-from-Shading methods, which improve a priori low-resolution topography by using optical images at the target resolution. We train SBs on a novel dataset of rendered lunar topography, emulating optical imagery from the Lunar Reconnaissance Orbiter Narrow Angle Camera. The result is a flexible approach for topography super-resolution which can provide pixel-level uncertainties in the reconstruction.

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