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MoonAnything: A Vision Benchmark with Large-Scale Lunar Supervised Data

Clémentine Grethen, Yuang Shi, Simone Gasparini, Géraldine Morin

Abstract

Accurate perception of lunar surfaces is critical for modern lunar exploration missions. However, developing robust learning-based perception systems is hindered by the lack of datasets that provide both geometric and photometric supervision. Existing lunar datasets typically lack either geometric ground truth, photometric realism, illumination diversity, or large-scale coverage. In this paper, we introduce MoonAnything, a unified benchmark built on real lunar topography with physically-based rendering, providing the first comprehensive geometric and photometric supervision under diverse illumination with large scale. The benchmark comprises two complementary sub-datasets : i) LunarGeo provides stereo images with corresponding dense depth maps and camera calibration enabling 3D reconstruction and pose estimation; ii) LunarPhoto provides photorealistic images using a spatially-varying BRDF model, along with multi-illumination renderings under real solar configurations, enabling reflectance estimation and illumination-robust perception. Together, these datasets offer over 130K samples with comprehensive supervision. Beyond lunar applications, MoonAnything offers a unique setting and challenging testbed for algorithms under low-textured, high-contrast conditions and applies to other airless celestial bodies and could generalize beyond. We establish baselines using state-of-the-art methods and release the complete dataset along with generation tools to support community extension: https://github.com/clementinegrethen/MoonAnything.

MoonAnything: A Vision Benchmark with Large-Scale Lunar Supervised Data

Abstract

Accurate perception of lunar surfaces is critical for modern lunar exploration missions. However, developing robust learning-based perception systems is hindered by the lack of datasets that provide both geometric and photometric supervision. Existing lunar datasets typically lack either geometric ground truth, photometric realism, illumination diversity, or large-scale coverage. In this paper, we introduce MoonAnything, a unified benchmark built on real lunar topography with physically-based rendering, providing the first comprehensive geometric and photometric supervision under diverse illumination with large scale. The benchmark comprises two complementary sub-datasets : i) LunarGeo provides stereo images with corresponding dense depth maps and camera calibration enabling 3D reconstruction and pose estimation; ii) LunarPhoto provides photorealistic images using a spatially-varying BRDF model, along with multi-illumination renderings under real solar configurations, enabling reflectance estimation and illumination-robust perception. Together, these datasets offer over 130K samples with comprehensive supervision. Beyond lunar applications, MoonAnything offers a unique setting and challenging testbed for algorithms under low-textured, high-contrast conditions and applies to other airless celestial bodies and could generalize beyond. We establish baselines using state-of-the-art methods and release the complete dataset along with generation tools to support community extension: https://github.com/clementinegrethen/MoonAnything.

Paper Structure

This paper contains 9 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Generation
  4. Common Pipeline Tools
  5. LunarGeo for Geometric Perception
  6. LunarPhoto for Photometric Perception
  7. MoonAnything Dataset
  8. Stereo 3D Reconstruction as a Showcase
  9. Conclusion

Figures (4)

  • Figure 1: Overview of the MoonAnything dataset generation pipeline.
  • Figure 2: Examples of LunarGeo illustrating the three trajectory types. The first row shows stereo pairs and their associated 3D scene over the lunar South Pole, rendered using the Hapke BRDF with constant parameters. The second row presents two stereo pairs from the Tycho crater: the first rendered using the classical Hapke BRDF, and the second using SVBRDF from grethen2026lunar.
  • Figure 3: Samples from LunarPhoto.
  • Figure 4: Examples of challenging cases: (a) extreme shadowing observed in the multi-lighting data from LunarPhoto, (b) a LunarGeo pair from the Tycho under raking lighting, and (c) a flat stereo pair from the S. Pole captured by LunarGeo.