Neural Scene Representations for Lunar Terrain Modeling
Description
We have developed LunarNRM, a novel neural surface reconstruction algorithm based on Neural Radiance Fields (NeRFs) that incorporates shadow-aware and depth-aware methodologies.
Detailed example
Our LunarNRM generates shadow-controlled Digital Elevation Models (DEMs) of the lunar surface, enabling accurate modeling and relighting of largely shadowed regions such as craters in the lunar south pole.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
By integrating multi-sensor data from the Lunar Reconnaissance Orbiter (LRO)—specifically optical data from the Narrow Angle Camera (NAC) and altimeter data from the Lunar Orbiter Laser Altimeter (LOLA)—we have demonstrated that LunarNRM can effectively reconstruct these critical regions, directly supporting NASA’s Artemis campaign.
Controls / human review
ATO: Not reported; PIA: Not published