Recently, Gaussian splatting has emerged as a strong alternative to NeRF, demonstrating impressive 3D modeling capabilities while requiring only a fraction of the training and rendering time. In this paper, we show how the standard Gaussian splatting framework can be adapted for remote sensing, retaining its high efficiency. This enables us to achieve state-of-the-art performance in just a few minutes, compared to the day-long optimization required by the best-performing NeRF-based Earth observation methods. The proposed framework incorporates remote-sensing improvements from EO-NeRF, such as radiometric correction and shadow modeling, while introducing novel components, including sparsity, view consistency, and opacity regularizations.
近年来，高斯点云技术（Gaussian Splatting）作为一种强有力的 NeRF 替代方法，以显著降低训练和渲染时间的优势，展现了卓越的三维建模能力。在本文中，我们展示了如何将标准的高斯点云框架适配于遥感领域，同时保持其高效性。这一改进使我们能够在短短几分钟内实现当前最先进的性能，相比之下，性能最佳的基于 NeRF 的地球观测方法通常需要耗时一天的优化过程。 该框架结合了来自 EO-NeRF 的遥感优化方法，包括辐射校正和阴影建模，同时引入了新的组件，如稀疏性约束、多视图一致性，以及不透明度正则化。这些改进使得我们的方法在保持高效率的同时，显著提升了遥感三维建模的精度和鲁棒性，为遥感数据的高效处理提供了新方案。