LNEM: Lunar Neural Elevation Model
Suwan Lee (KENTECH), Jo Ryeong Yim (KARI), Kibaek Park (KENTECH), Dong-Gyu Kim (KARI), Eunhyeuk Kim (KARI), Minsup Jeong (KASI), Chae Kyung Sim (KASI), Seokju Lee (KENTECH)
IEEE/CVF Conference on Computer Vision and Pattern RecognitionΒ (CVPR), 2026
We introduce LNEM, a neural elevation model for lunar terrain reconstruction that explicitly incorporates rigorous pushbroom sensor modeling to enforce multi-orbit consistency. By modeling pushbroom imaging geometry, LNEM reconstructs high-fidelity and geometrically consistent elevations from sparse multi-orbit imagery. We further present Lunar Studio, a standardized multi-orbit benchmark integrating LROC NAC and KPLO LUTI observations across diverse lunar regions and illumination conditions. LNEM preserves terrain contours and maintains stable reconstruction quality under varying viewing geometries.