Illuminating the Moon: Reconstruction of lunar terrain using photogrammetry, Neural Radiance Fields, and Gaussian Splatting

Abstract

Accurately reconstructing the lunar surface is critical for scientific analysis and the planning of future lunar missions. This study investigates the efficacy of three advanced reconstruction techniques – photogrammetry, Neural Radiance Fields, and Gaussian Splatting – applied to the lunar surface imagery. The research emphasizes the influence of varying illumination conditions and shadows, crucial elements due to the Moon's lack of atmosphere. Extensive comparative analysis is conducted using a dataset of lunar surface images captured under different lighting scenarios. Our results demonstrate the strengths and weaknesses of each method based on a pairwise comparison of the obtained models with the original one. The results indicate that using methods based on neural networks, it is possible to complement the model obtained by classical photogrammetry. These insights are invaluable for the optimization of surface reconstruction algorithms, promoting enhanced accuracy and reliability in the context of upcoming lunar exploration missions.