Reconstruction under adverse rainy conditions poses significant challenges due to reduced visibility and the distortion of visual perception. These conditions can severely impair the quality of geometric maps, which is essential for applications ranging from autonomous planning to environmental monitoring. In response to these challenges, this study introduces the novel task of 3D Reconstruction in Rainy Environments (3DRRE), specifically designed to address the complexities of reconstructing 3D scenes under rainy conditions. To benchmark this task, we construct the HydroViews dataset that comprises a diverse collection of both synthesized and real-world scene images characterized by various intensities of rain streaks and raindrops. Furthermore, we propose DeRainGS, the first 3DGS method tailored for reconstruction in adverse rainy environments. Extensive experiments across a wide range of rain scenarios demonstrate that our method delivers state-of-the-art performance, remarkably outperforming existing occlusion-free methods.
在恶劣的雨天条件下进行重建由于能见度降低和视觉感知的扭曲而面临重大挑战。这些条件可能严重影响几何地图的质量，而几何地图对于从自动规划到环境监测的各种应用至关重要。针对这些挑战，本研究引入了一个新颖的任务——雨天环境下的三维重建（3DRRE），专门用于解决雨天条件下三维场景重建的复杂性。为评估这一任务，我们构建了 HydroViews 数据集，该数据集包含了多种强度的雨痕和雨滴特征的合成和真实世界场景图像。此外，我们提出了 DeRainGS，这是首个专为恶劣雨天环境中的重建设计的 3DGS 方法。在各种雨天场景下的大量实验表明，我们的方法提供了最先进的性能，显著优于现有的无遮挡方法。