1 citations · 2 across the 6 of their papers we have counts for
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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…
Sketch&Patch++: Efficient Structure-Aware 3D Gaussian Representation
Yuang Shi, Géraldine Morin, Simone Gasparini +1
We observe that Gaussians exhibit distinct roles and characteristics analogous to traditional artistic techniques -- like how artists first sketch outlines before filling in broade…
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…
ROI-NeRFs: Hi-Fi Visualization of Objects of Interest within a Scene by NeRFs Composition
Quoc-Anh Bui, Gilles Rougeron, Géraldine Morin +1
Efficient and accurate 3D reconstruction is essential for applications in cultural heritage. This study addresses the challenge of visualizing objects within large-scale scenes at…
Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes
Yuang Shi, Simone Gasparini, Géraldine Morin +2
3D Gaussian Splatting (3DGS) has emerged as a promising representation for photorealistic rendering of 3D scenes. However, its high storage requirements pose significant challenges…