为虚拟现实重建火星环境的红色星球修复：扩散模型
Inpainting the Red Planet: Diffusion Models for the Reconstruction of Martian Environments in Virtual Reality
AI总结：
本文提出基于无条件扩散模型的火星表面重建方法，利用增强数据集和非均匀重缩放策略，优于传统空洞填充技术，在重建精度和感知相似性上表现更优。
摘要：
太空探索日益依赖虚拟现实用于多个任务，如任务规划、跨学科科学分析和宇航员培训。模拟可靠性的关键因素是拥有准确的三维行星地形表示。从卫星图像中获得的外星高度图往往由于获取和传输限制而包含缺失值。火星是除地球外研究最广泛的行星之一，其广泛的地形数据集使其成为有价值的火星表面重建任务，尽管许多区域仍未测绘。深度学习算法可以支持空洞填充任务；然而，尽管地球的全面数据集允许使用条件方法，但这些方法无法应用于火星。当前的方法依赖于更简单的插值技术，但这些技术往往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的火星表面重建方法。训练是在由NASA的HiRISE调查衍生出的12000个增强数据集上进行的。非均匀重缩放策略在多个尺度上捕捉地形特征，然后将其调整为固定的128x128模型分辨率。我们对比了我们的方法与已建立的空洞填充和修复技术，包括反距离加权、克里格和纳维-斯托克斯算法，在1000个样本的评估集上。结果表明，我们的方法在重建准确性（RMSE上4-15%）和感知相似性（LPIPS上29-81%）方面均优于这些方法。
英文摘要：
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.
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