Space exploration increasingly relies on Virtual Reality for several tasks, such as mission planning, multidisciplinary scientific analysis, and astronaut training. A key factor for the reliability of the simulations is having accurate 3D representations of planetary terrains. Extraterrestrial heightmaps derived from satellite imagery often contain missing values due to acquisition and transmission constraints. Mars is among the most studied planets beyond Earth, and its extensive terrain datasets make the Martian surface reconstruction a valuable task, although many areas remain unmapped. Deep learning algorithms can support void-filling tasks; however, whereas Earth's comprehensive datasets enables the use of conditional methods, such approaches cannot be applied to Mars. Current approaches rely on simpler interpolation techniques which, however, often fail to preserve geometric coherence. In this work, we propose a method for reconstructing the surface of Mars based on an unconditional diffusion model. Training was conducted on an augmented dataset of 12000 Martian heightmaps derived from NASA's HiRISE survey. A non-homogeneous rescaling strategy captures terrain features across multiple scales before resizing to a fixed 128x128 model resolution. We compared our method against established void-filling and inpainting techniques, including Inverse Distance Weighting, kriging, and Navier-Stokes algorithm, on an evaluation set of 1000 samples. Results show that our approach consistently outperforms these methods in terms of reconstruction accuracy (4-15% on RMSE) and perceptual similarity (29-81% on LPIPS) with the original data.

翻译：太空探索日益依赖虚拟现实技术完成多项任务，如任务规划、多学科科学分析和宇航员训练。模拟可靠性的关键因素在于获得精确的行星地形三维表征。源自卫星影像的地外高度图常因采集与传输限制而存在数据缺失。火星是除地球外研究最深入的行星，其广泛的地形数据集使火星表面重建成为极具价值的任务，尽管许多区域仍未被测绘。深度学习算法可支持空洞填补任务；然而，尽管地球的完备数据集支持条件化方法的应用，此类方法却无法适用于火星。现有方法依赖于较简单的插值技术，但这些技术往往难以保持几何一致性。本研究提出一种基于无条件扩散模型的火星表面重建方法。训练使用由NASA HiRISE勘测数据衍生的12000幅火星高度图增强数据集进行。采用非均匀重缩放策略在调整至固定128x128模型分辨率前捕获多尺度地形特征。我们在包含1000个样本的评估集上，将本方法与现有空洞填补和修复技术（包括反距离加权法、克里金法和纳维-斯托克斯算法）进行比较。结果表明，在重建精度（RMSE指标提升4-15%）和感知相似性（LPIPS指标提升29-81%）方面，本方法均持续优于这些对比方法。