用高斯点云加速卫星影像建模，几分钟完成此前需数小时的工作。
Gaussian Splatting for Efficient Satellite Image Photogrammetry
- 基于高斯点云构建高效3D建模框架，替代传统NeRF方法。
- 在卫星影像上实现顶尖性能，训练时间缩短至几分钟。
- 新增稀疏性、视角一致性等正则化，提升遥感建模精度。
最近，高斯点云渲染成为NeRF的有力替代方案，展现出出色的三维建模能力，同时训练与渲染时间大幅缩减。本文展示如何将标准高斯点云框架适配于遥感领域，保持其高效率。该方法仅需几分钟即可达到当前最优性能，远快于此前最先进的基于NeRF的地球观测方法所需的数日优化时间。所提框架融合了EO-NeRF中的辐射校正与阴影建模等遥感改进技术，并引入新的正则化项，包括稀疏性、视角一致性和不透明度正则化，显著提升了建模质量与稳定性。
原文摘要 · Abstract (English)
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.
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