Abstract
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.
Abstract (translated)
空间探索越来越依赖虚拟现实技术来完成多项任务，如任务规划、跨学科科学分析和宇航员训练。模拟的可靠性关键在于拥有准确的三维地形表示。从卫星图像获得的地外高程图（高度地图）通常会因为获取和传输限制而包含缺失值。火星是地球以外研究最为深入的行星之一，其广泛的地形数据集使得火星表面重建成为一个有价值的课题，尽管许多地区仍未绘制完毕。深度学习算法可以支持填补空缺的任务；然而，由于地球详尽的数据集能够使用条件方法，这种方法却不能应用于火星。当前的方法依赖于较为简单的插值技术，但这些技术通常无法保持几何一致性。 在本工作中，我们提出了一种基于无条件扩散模型重建火星表面的方法。训练是在一个扩充的由NASA的HiRISE调查数据衍生出的12000张火星高程图的数据集上进行的。通过非均匀重缩放策略，在将图像调整为固定的128x128分辨率之前，捕获不同尺度下的地形特征。我们在包含1000个样本的评估集中比较了我们的方法与已建立的填补空缺和修复技术，包括反距离加权法、克里金插值和Navier-Stokes算法。结果显示，无论是在重建准确性（以RMSE衡量，提高了4-15%）还是感知相似性（以LPIPS衡量，提高了29-81%），我们的方法均优于这些方法与原始数据对比的结果。 此研究展示了利用深度学习模型在火星表面重建任务中的巨大潜力，并为未来进一步探索提供了可能的方向。
URL
https://arxiv.org/abs/2510.14765
