SatUnreal：基于虚幻引擎的卫星立体匹配高精度合成数据集
SatUnreal: A High-Precision Synthetic Dataset for Satellite Stereo Matching via Unreal Engine
- TelePIX
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于虚幻引擎的合成数据集SatUnreal，通过物理几何模拟、时空一致性、地形多样性和数学标签完整性生成高精度立体图像对，训练模型在真实基准上取得优于真实数据的零样本迁移性能。
AI中文摘要：
从卫星图像进行三维重建对于大规模地形分析至关重要，然而缺乏具有精确遮挡标签的高保真训练数据集仍是主要瓶颈。现有的基准数据集，如US3D和WHU-Stereo，面临时空不一致的内在挑战——多视角采集之间的环境变化和阴影位移——并且由于LiDAR点云稀疏性，在遮挡区域提供的真值存在模糊性。在本文中，我们提出SatUnreal，一个高精度合成数据集，旨在通过基于虚幻引擎的模拟流程从根本上克服这些限制。SatUnreal提供10,000对立体图像，具有高分辨率（0.3m GSD），并具有以下特点：（1）物理几何模拟，通过系统地改变基线和方位角来复现真实的卫星轨道；（2）时空一致性，通过固定的虚拟环境消除时间噪声；（3）地形多样性，覆盖从密集城市峡谷到低纹理自然地形的多种场景；（4）数学标签完整性，利用一种新颖的两步线追踪算法生成无瑕的遮挡掩膜。使用最先进的迭代模型的实验结果表明，仅使用SatUnreal训练的模型在真实世界基准（US3D，WHU-Stereo）上的零样本迁移性能优于使用真实数据集训练的模型。我们的研究证明，物理上准确的合成数据为学习几何特征提供了比复杂真实世界观测更有效的监督信号，为地球观测中的Sim-to-Real迁移建立了新范式。代码和数据集可在https://github.com/jmp-Telepix/SatUnreal_A_High-Precision_Synthetic_Dataset_for_Satellite_Stereo_Matching_via_UnrealEngine获取。
英文摘要：
3D reconstruction from satellite imagery is essential for large-scale topographic analysis, yet the lack of high-fidelity training datasets with accurate occlusion labels remains a primary bottleneck. Existing benchmarks, such as US3D and WHU-Stereo, face inherent challenges in spatio-temporal mismatch -- environmental changes and shadow displacements between multi-view acquisitions -- and provide ambiguous ground truth in occluded regions due to LiDAR sparsity. In this paper, we propose SatUnreal, a high-precision synthetic dataset designed to fundamentally overcome these limitations through an Unreal Engine-based simulation pipeline. SatUnreal provides 10,000 stereo pairs with high resolution (0.3m GSD) and is characterized by: (1) Physical Geometry Simulation, replicating realistic satellite orbits by systematically varying baselines and azimuths; (2) Spatio-temporal Consistency, eliminating temporal noise through fixed virtual environments; (3) Topographic Diversity, spanning dense urban canyons to low-texture natural terrains; and (4) Mathematical Label Integrity, utilizing a novel two-step linetrace algorithm to generate flawless occlusion masks. Experimental results using SOTA iterative models demonstrate that models trained exclusively on SatUnreal achieve superior zero-shot transfer performance on real-world benchmarks (US3D, WHU-Stereo) compared to those trained on real datasets. Our findings prove that physically accurate synthetic data provides a more effective supervisory signal for learning geometric features than complex real-world observations, establishing a new paradigm for Sim-to-Real transfer in Earth Observation. Code and dataset are available at https://github.com/jmp-Telepix/SatUnreal_A_High-Precision_Synthetic_Dataset_for_Satellite_Stereo_Matching_via_UnrealEngine
