发表机构
University of Technology of Troyes; Hochschule Darmstadt; European Space Agency; Technische Hochschule Würzburg-Schweinfurt（特鲁瓦技术大学; 达姆施塔特应用科学大学; 欧洲空间局; 维尔茨堡-施韦因富特应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过微调零样本相对深度估计模型Depth Anything V2，利用公开立体摄影测量生成的月球DEM数据，实现了更精准的月球表面高度估计，为月球着陆危险定位提供支持。
AI 中文摘要
数字高程模型（DEM）可提供精确的高度信息，对月球表面分析具有重要价值。欧洲空间局（ESA）正筹备未来月球着陆任务，精准的高度估计方法对可能威胁着陆的危险地形至关重要。传统从影像生成DEM的方法，如明暗恢复形状（SfS）和立体摄影测量（SPG），已被证明对该任务极具价值。但随着机器学习尤其是计算机视觉的发展，研究重心转向基于深度学习的单目深度估计。月球表面遍布岩石与陨石坑，传统危险检测方法仅依赖2D图像数据，本研究旨在开发相对月球表面高度估计器，为危险定位提供额外信息。本文提出的方法基于知名的零样本相对深度估计模型Depth Anything V2（DAV2），其他研究将其作为其提出的月球DEM估计方法的最先进对比基准，但未针对目标领域进行适配，因此可能表现不佳。为此，本研究提出一种微调策略，使用公开可用的由SPG生成的月球表面DEM数据。结果表明，与零样本模型相比，性能显著提升，有效将DAV2转化为可靠的月球表面相对深度估计器。
英文摘要
Digital elevation models (DEMs) can provide accurate height information, making it invaluable for analyzing the lunar surface. As the European Space Agency (ESA) prepares for future lunar missions that aim to land on the Moon, a precise method for height estimation will be essential for hazardous terrain that could endanger the landing approach. Traditional approaches to generate DEMs from imagery, such as shape from shading (SfS) and stereophotogrammetry (SPG) have been proven highly valuable for this task. However, due to advancements in machine learning, especially computer vision, the focus has shifted towards monocular depth estimation via deep learning. The lunar surface is covered by rocks and craters, and classic hazard detection methods rely solely on 2D image data. Our goal is to address this issue by developing a relative lunar surface height estimator that can provide additional information for hazard localization. In this letter, we present a methodology that builds on the well-known zero-shot relative depth estimation model Depth Anything V2 (DAV2). Other works have been using it as a state-of-the-art comparison for their proposed lunar DEM estimation method, but without adaptations to the target domain. Thus, it may underperform. Therefore, we propose a fine-tuning strategy with publicly available SPG-derived DEM data of the lunar surface. Our results demonstrate a significant improvement in performance compared to the zero-shot model, effectively transforming DAV2 into a reliable relative depth estimator of the lunar surface.
