Topographic models are essential for characterizing planetary surfaces and for inferring underlying geological processes. Nevertheless, meter-scale topographic data remain limited, which constrains detailed planetary investigations, even for the Moon, where extensive high-resolution orbital images are available. Recent advances in deep learning (DL) exploit single-view imagery, constrained by low-resolution topography, for fast and flexible reconstruction of fine-scale topography. However, their robustness and general applicability across diverse lunar landforms and illumination conditions remain insufficiently explored. In this study, we build upon our previously proposed DL framework by incorporating a more robust scale recovery scheme and extending the model to polar regions under low solar illumination conditions. We demonstrate that, compared with single-view shape-from-shading methods, the proposed DL approach exhibits greater robustness to varying illumination and achieves more consistent and accurate topographic reconstructions. Furthermore, it reliably reconstructs topography across lunar features of diverse scales, morphologies, and geological ages. High-quality topographic models are also produced for the lunar south polar areas, including permanently shadowed regions, demonstrating the method's capability in reconstructing complex and low-illumination terrain. These findings suggest that DL-based approaches have the potential to leverage extensive lunar datasets to support advanced exploration missions and enable investigations of the Moon at unprecedented topographic resolution.

翻译：地形模型对于表征行星表面特征及推断其潜在地质过程至关重要。然而，米级尺度的地形数据仍然有限，这制约了详细的行星探测研究，即使对于已获取大量高分辨率轨道影像的月球而言亦是如此。深度学习的最新进展利用单视角影像，辅以低分辨率地形数据作为约束，实现了快速、灵活的高分辨率地形重建。然而，这些方法在不同月球地貌和光照条件下的鲁棒性与普适性仍未得到充分探究。在本研究中，我们在先前提出的深度学习框架基础上，引入了更鲁棒的尺度恢复方案，并将模型扩展至低太阳光照条件下的极区。我们证明，相较于单视角从明暗恢复形状的方法，所提出的深度学习方案对变化的光照条件表现出更强的鲁棒性，并能实现更一致、更准确的地形重建。此外，该方法能够可靠地重建不同尺度、形态及地质年龄的月球特征地形。针对月球南极区域，包括永久阴影区，该方法亦能生成高质量地形模型，证明了其在重建复杂及低光照地形方面的能力。这些发现表明，基于深度学习的方法具备利用海量月球数据集的潜力，可为高级探测任务提供支持，并以前所未有的地形分辨率实现月球探测研究。