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.

翻译：近年来，高斯泼溅技术已成为NeRF的有力替代方案，在仅需少量训练与渲染时间的同时，展现出卓越的三维建模能力。本文展示了如何将标准高斯泼溅框架适配于遥感领域，并保持其高效性。这使得我们能够在数分钟内实现最先进的性能，而基于NeRF的现有最优地球观测方法则需要长达数日的优化过程。所提出的框架融合了EO-NeRF中的遥感改进技术，如辐射校正与阴影建模，同时引入了包括稀疏性约束、视角一致性约束与不透明度正则化在内的新型组件。