DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909142864625664 |
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| author | Zhou, Xiaoyu Lin, Zhiwei Shan, Xiaojun Wang, Yongtao Sun, Deqing Yang, Ming-Hsuan |
| author_facet | Zhou, Xiaoyu Lin, Zhiwei Shan, Xiaojun Wang, Yongtao Sun, Deqing Yang, Ming-Hsuan |
| contents | We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://github.com/VDIGPKU/DrivingGaussian. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07920 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes Zhou, Xiaoyu Lin, Zhiwei Shan, Xiaojun Wang, Yongtao Sun, Deqing Yang, Ming-Hsuan Computer Vision and Pattern Recognition We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://github.com/VDIGPKU/DrivingGaussian. |
| title | DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.07920 |