← 研踪 PaperTrailCVF · CVPR 2026:cvf:3f75ab493929dbdb1e24aa699a9fca65 · 记录 #1
LNEM: Lunar Neural Elevation Model
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:43
摘要
High-resolution and high-precision digital elevation models (DEMs) of the lunar surface are essential for landing site selection and geological research. However, traditional stereo matching provides a limited representation of the 3D scene and struggling with non-textured regions and extreme illumination variations. Recent lunar neural rendering methods are also ill-suited for 3D reconstruction due to their reliance on simple pinhole approximations for pushbroom sensors. These challenges are further compounded by geometric misalignment, distributional bias, and labor-intensive handcrafted preprocessing in satellite image pipelines. To address these issues, we introduce the Lunar Neural Elevation Model (LNEM), a volumetric reconstruction method that explicitly incorporates the pushbroom imaging process. A core component of our approach is Lunar Studio, a multi-orbit dataset and pipeline constructed using Rigorous Sensor Models (RSMs) to produce geometrically consistent observations from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) and the Korea Pathfinder Lunar Orbiter (KPLO) Lunar Terrain Imager (LUTI). LNEM integrates this pushbroom camera formulation with learned shadow modeling, enabling geometrically grounded and illumination-aware volumetric rendering under challenging lunar lighting conditions. Extensive experiments demonstrate that LNEM achieves geometrically consistent reconstruction across multiple sensors under diverse viewing and illumination conditions, providing a scalable complement to conventional DEM pipelines. To support reproducibility and future lunar research, we release Lunar Studio, the multi-orbit dataset, and the LNEM reconstruction pipeline.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
LNEM: Lunar Neural Elevation Model
High-resolution and high-precision digital elevation models (DEMs) of the lunar surface are essential for landing site selection and geological research. However, traditional stereo matching provides a limited representation of the 3D scene and struggling with non-textured regions and extreme illumination variations. Recent lunar neural rendering methods are also ill-suited for 3D reconstruction due to their reliance on simple pinhole approximations for pushbroom sensors. These challenges are further compounded by geometric misalignment, distributional bias, and labor-intensive handcrafted preprocessing in satellite image pipelines. To address these issues, we introduce the Lunar Neural Elevation Model (LNEM), a volumetric reconstruction method that explicitly incorporates the pushbroom imaging process. A core component of our approach is Lunar Studio, a multi-orbit dataset and pipeline constructed using Rigorous Sensor Models (RSMs) to produce geometrically consistent observations from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) and the Korea Pathfinder Lunar Orbiter (KPLO) Lunar Terrain Imager (LUTI). LNEM integrates this pushbroom camera formulation with learned shadow modeling, enabling geometrically grounded and illumination-aware volumetric rendering under challenging lunar lighting conditions. Extensive experiments demonstrate that LNEM achieves geometrically consistent reconstruction across multiple sensors under diverse viewing and illumination conditions, providing a scalable complement to conventional DEM pipelines. To support reproducibility and future lunar research, we release Lunar Studio, the multi-orbit dataset, and the LNEM reconstruction pipeline.
查看此版本解读 →查看此版本原文 ↗