DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments
This addresses the challenge of accurate 3D reconstruction for applications like autonomous planning and environmental monitoring in adverse weather, though it is incremental as it adapts an existing method to a new condition.
The paper tackles the problem of 3D scene reconstruction in rainy environments, which reduces visibility and distorts perception, by introducing the DeRainGS method and HydroViews dataset, achieving state-of-the-art performance that outperforms existing occlusion-free methods.
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.