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Geometric Foundation Model Distillation for Efficient Lunar 3D Reconstruction
Clémentine Grethen, Florient Chouteau, Géraldine Morin +1
Large 3D foundation models such as MASt3R achieve state-of-the-art stereo reconstruction but are computationally demanding for deployment under strict hardware constraints -- a cri…
MoonAnything: A Vision Benchmark with Large-Scale Lunar Supervised Data
Clémentine Grethen, Yuang Shi, Simone Gasparini +1
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 dat…
Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation
Clementine Grethen, Nicolas Menga, Roland Brochard +4
We address the problem of estimating realistic, spatially varying reflectance for complex planetary surfaces such as the lunar regolith, which is critical for high-fidelity renderi…
Adapting Stereo Vision From Objects To 3D Lunar Surface Reconstruction with the StereoLunar Dataset
Clementine Grethen, Simone Gasparini, Geraldine Morin +3
Accurate 3D reconstruction of lunar surfaces is essential for space exploration. However, existing stereo vision reconstruction methods struggle in this context due to the Moon's l…