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DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments
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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.
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Cited by 3 Pith papers
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DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views
DerainSplat reconstructs clean 3D Gaussian scenes from sparse rainy views in a single forward pass by predicting weather factors and using support maps to guide matching and appearance fusion.
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Weather-Magician: Reconstruction and Rendering Framework for 4D Weather Synthesis In Real Time
A real-time framework that reconstructs clear scenes with 3D Gaussian Splatting and renders them under controllable fog, rain, snow, and snow-cover effects.
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Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction
Momentum-GS improves large-scale 3D Gaussian splatting by using a momentum teacher decoder and reconstruction-guided block weighting to boost reconstruction quality and reduce memory use.
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