Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation

TL;DR

Lunar-G2R introduces a novel geometry-to-reflectance learning framework that estimates detailed, spatially varying lunar surface reflectance directly from elevation data, enhancing photorealism without requiring multi-view images or special hardware.

Contribution

It is the first method to infer spatially varying lunar BRDF parameters directly from terrain geometry using a differentiable rendering approach.

Findings

Reduces photometric error by 38% compared to baseline

Achieves higher PSNR and SSIM in lunar surface rendering

Captures fine-scale reflectance variations absent in uniform models

Abstract

We address the problem of estimating realistic, spatially varying reflectance for complex planetary surfaces such as the lunar regolith, which is critical for high-fidelity rendering and vision-based navigation. Existing lunar rendering pipelines rely on simplified or spatially uniform BRDF models whose parameters are difficult to estimate and fail to capture local reflectance variations, limiting photometric realism. We propose Lunar-G2R, a geometry-to-reflectance learning framework that predicts spatially varying BRDF parameters directly from a lunar digital elevation model (DEM), without requiring multi-view imagery, controlled illumination, or dedicated reflectance-capture hardware at inference time. The method leverages a U-Net trained with differentiable rendering to minimize photometric discrepancies between real orbital images and physically based renderings under known viewing and illumination geometry. Experiments on a geographically held-out region of the Tycho crater show that our approach reduces photometric error by 38 % compared to a state-of-the-art baseline, while achieving higher PSNR and SSIM and improved perceptual similarity, capturing fine-scale reflectance variations absent from spatially uniform models. To our knowledge, this is the first method to infer a spatially varying reflectance model directly from terrain geometry.