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The paper introduces MoonAnything, a large-scale lunar benchmark dataset with both geometric and photometric supervision, addressing the lack of comprehensive datasets for training learning-based lunar perception systems. It comprises two sub-datasets: LunarGeo, providing stereo images with dense depth maps, and LunarPhoto, offering photorealistic images with diverse illumination. Experiments using state-of-the-art methods establish baselines on this new benchmark, which includes over 130K samples and generation tools for community extension.
Training robust lunar rovers just got easier: MoonAnything provides the first large-scale, photorealistic lunar dataset with paired depth maps and diverse lighting conditions.
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.
