DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909328304242688 |
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| author | Liu, Shuhong Chen, Xiang Chen, Hongming Xu, Quanfeng Li, Mingrui |
| author_facet | Liu, Shuhong Chen, Xiang Chen, Hongming Xu, Quanfeng Li, Mingrui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11540 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments Liu, Shuhong Chen, Xiang Chen, Hongming Xu, Quanfeng Li, Mingrui Computer Vision and Pattern Recognition 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. |
| title | DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.11540 |