月面環境における単眼深度推定の有効性
Efficacy of Monocular Depth Estimation in Lunar Environments
月面のアナログデータとアポロ17号の画像を用い、最先端の単眼深度推定手法が未知の月面地形を高精度に3次元再構成できるか検証した。
著者: Michael W. Lanighan, Kenji Brameld, Seth Gee
分類: cs.RO, cs.CV
原文アブストラクト
Recent advances in monocular depth estimation have demonstrated highly accurate 3D reconstructions within in-distribution terrestrial domains. This study investigates the efficacy of several state-of-the-art approaches to reconstruct out-of-distribution lunar environments using the NASA Ames Polar Optical Lunar Analog Reconstruction (POLAR) stereo dataset and Apollo 17 surface imagery. By leveraging these datasets, we establish a robust analog-to-field validation pipeline that evaluates quantitative performance against ground-truth physical analogs and qualitative performance against historical field data. Our results indicate that select state-of-the-art techniques can successfully generalize to produce high-fidelity relative 3D reconstructions of the unstructured lunar surface.