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

arXiv: https://arxiv.org/abs/2601.10449 · HTML: https://mlsanity.com/paper/2601.10449/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021)
- [Single-Image SVBRDF Capture with a Rendering-Aware Deep Network](https://mlsanity.com/paper/1810.09718/) (2018)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Neural BRDF Representation and Importance Sampling](https://mlsanity.com/paper/2102.05963/) (2021)
- [Material Palette: Extraction of Materials from a Single Image](https://mlsanity.com/paper/2311.17060/) (2023)
- [Scientific image rendering for space scenes with the SurRender software](https://mlsanity.com/paper/1810.01423/) (2018)
- [Hypernetworks for Generalizable BRDF Representation](https://mlsanity.com/paper/2311.15783/) (2023)
- [LuSNAR:A Lunar Segmentation, Navigation and Reconstruction Dataset based on Muti-sensor for Autonomous Exploration](https://mlsanity.com/paper/2407.06512/) (2024)
- [High performance Lunar landing simulations](https://mlsanity.com/paper/2409.11450/) (2024)
- [On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches](https://mlsanity.com/paper/2502.15480/) (2025)
- [Adapting Stereo Vision From Objects To 3D Lunar Surface Reconstruction with the StereoLunar Dataset](https://mlsanity.com/paper/2510.18172/) (2025)

## Built on by (most cited first, 1 of 1)

- [MoonAnything: A Vision Benchmark with Large-Scale Lunar Supervised Data](https://mlsanity.com/paper/2604.00682/) (2026)

## Abstract (arXiv)

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.