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

TL;DR

MoonAnything is a large-scale lunar dataset with geometric and photometric supervision, designed to advance perception systems for lunar exploration and other celestial bodies.

Contribution

It introduces the first comprehensive lunar benchmark with diverse supervision, including stereo images, depth maps, photorealistic images, and illumination variations.

Findings

Established baseline performance with state-of-the-art methods.

Provided over 130,000 samples for lunar perception tasks.

Released dataset and tools for community use.

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.