CVApr 1

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

arXiv:2604.0068252.1h-index: 3Has Code
AI Analysis

This addresses a data bottleneck for lunar exploration missions and offers a testbed for algorithms in low-textured, high-contrast conditions, though it is incremental as it builds on existing rendering and dataset creation methods.

The paper tackles the lack of datasets for lunar perception by introducing MoonAnything, a benchmark with over 130K samples providing geometric and photometric supervision under diverse illumination, enabling tasks like 3D reconstruction and reflectance estimation.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes