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.

翻译：我们致力于解决复杂行星表面（如月球风化层）的真实、空间变化反射率估计问题，这对高保真渲染和基于视觉的导航至关重要。现有的月球渲染流程依赖于简化或空间均匀的BRDF模型，其参数难以估计且无法捕捉局部反射率变化，限制了光度真实感。我们提出Lunar-G2R，一种几何到反射率的学习框架，可直接从月球数字高程模型（DEM）预测空间变化的BRDF参数，无需在推理时使用多视角图像、受控照明或专用反射率采集硬件。该方法利用通过可微分渲染训练的U-Net，在已知观测与照明几何条件下，最小化真实轨道图像与基于物理的渲染之间的光度差异。在Tycho陨石坑地理隔离区域的实验表明，相较于最先进的基线方法，我们的方法将光度误差降低了38%，同时实现了更高的PSNR与SSIM指标及更优的感知相似度，能够捕捉空间均匀模型所缺失的精细尺度反射率变化。据我们所知，这是首个直接从地形几何推断空间变化反射率模型的方法。