用多时相卫星图实现高效通用3D重建，无需真实高程数据
SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Images
- 将RPC模型融入通用3D高斯溅射框架，提升稀疏几何信息利用效率
- 在DFC19数据集上将平均误差从13.18米降至1.80米，速度提升86倍
- 自监督设计适合无真值高程的卫星影像，跨数据集泛化能力强
从稀疏视角卫星图像进行三维场景重建是一项长期且具有挑战性的任务。尽管3D高斯溅射（3DGS）及其变体因其高效性受到关注，但现有方法因不兼容有理多项式系数（RPC）模型且泛化能力有限，难以应用于卫星图像。近期通用3DGS方法虽具潜力，但在多时相稀疏卫星图像上表现不佳，受限于几何约束不足、临时物体干扰及辐射不一致。为此，我们提出SkySplat，一种新型自监督框架，将RPC模型集成至通用3DGS流程中，更有效地利用稀疏几何线索以提升重建质量。SkySplat仅依赖RGB图像与辐射鲁棒的相对高度监督，无需真值高程图。核心组件包括基于一致性掩码的交叉自一致性模块（CSCM），可缓解临时物体干扰；以及多视图一致性聚合策略，优化重建结果。相比逐场景优化方法，SkySplat在EOGS基础上实现86倍加速，精度更高。其在DFC19数据集上将平均绝对误差（MAE）从13.18米显著降低至1.80米，并在MVS3D基准上展现出强跨数据集泛化能力。
原文摘要 · Abstract (English)
Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS) and its variants have recently attracted attention for its high efficiency, existing methods remain unsuitable for satellite images due to incompatibility with rational polynomial coefficient (RPC) models and limited generalization capability. Recent advances in generalizable 3DGS approaches show potential, but they perform poorly on multi-temporal sparse satellite images due to limited geometric constraints, transient objects, and radiometric inconsistencies. To address these limitations, we propose SkySplat, a novel self-supervised framework that integrates the RPC model into the generalizable 3DGS pipeline, enabling more effective use of sparse geometric cues for improved reconstruction. SkySplat relies only on RGB images and radiometric-robust relative height supervision, thereby eliminating the need for ground-truth height maps. Key components include a Cross-Self Consistency Module (CSCM), which mitigates transient object interference via consistency-based masking, and a multi-view consistency aggregation strategy that refines reconstruction results. Compared to per-scene optimization methods, SkySplat achieves an 86 times speedup over EOGS with higher accuracy. It also outperforms generalizable 3DGS baselines, reducing MAE from 13.18 m to 1.80 m on the DFC19 dataset significantly, and demonstrates strong cross-dataset generalization on the MVS3D benchmark. The is available at https://github.com/NanCheng2001/SkySplat-main
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