Improving Lunar Topography with Deep Learning Schrödinger Bridges
This work addresses the need for high-resolution planetary topography models for geomorphological analysis, offering a scalable alternative to expensive analytical methods.
The authors developed a diffusion-based Schrödinger Bridge generative model for lunar topography super-resolution, achieving improved resolution by incorporating optical imagery. The method provides pixel-level uncertainties and is scalable due to hardware acceleration.
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 Schrödinger 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.