LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation

Aytaç Sekmen, Fatih Emre Gunes, Furkan Horoz, Hüseyin Umut Işık, Mehmet Alp Ozaydin, Onur Altay Topaloglu, Şahin Umutcan Üstündaş, Yurdasen Alp Yeni, Halil Ersin Soken, Erol Sahin, Ramazan Gokberk Cinbis, Sinan Kalkan

Keywords: Monocular Depth Estimation, Space Robotics, Benchmarking, Domain Adaptation, Lunar Exploration, Extraterrestrial Perception

TL;DR: We introduce a comprehensive benchmarking for lunar Monocular Depth Estimation (MDE), featuring novel real and synthetic datasets, systematic evaluations of state-of-the-art models, and baselines for sim-to-real domain adaptation tasks.

Abstract: Monocular Depth Estimation (MDE) is crucial for autonomous lunar rover navigation using electro-optical cameras. However, deploying terrestrial MDE networks to the Moon brings a severe domain gap due to harsh shadows, textureless regolith, and zero atmospheric scattering. Existing evaluations rely on analogs that fail to replicate these conditions and lack actual metric ground truth. To address this, we present LuMon, a comprehensive benchmarking framework to evaluate MDE methods for lunar exploration. We introduce novel datasets featuring high-quality stereo ground truth depth from the real Chang'e-3 mission and the CHERI dark analog dataset. Utilizing this framework, we conduct a systematic zero-shot evaluation of state-of-the-art architectures across synthetic, analog, and real datasets. We rigorously assess performance against mission critical challenges like craters, rocks, extreme shading, and varying depth ranges. Furthermore, we establish a sim-to-real domain adaptation baseline by fine tuning a foundation model on synthetic data. While this adaptation yields drastic in-domain performance gains, it exhibits minimal generalization to authentic lunar imagery, highlighting a persistent cross-domain transfer gap. Our extensive analysis reveals the inherent limitations of current networks and sets a standard foundation to guide future advancements in extraterrestrial perception and domain adaptation.

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Submission Number: 14