Stereo 3D Gaussian Splatting SLAM for Outdoor Urban Scenes
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915420139683840 |
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| author | Li, Xiaohan Gong, Ziren Tosi, Fabio Poggi, Matteo Mattoccia, Stefano Liu, Dong Wu, Jun |
| author_facet | Li, Xiaohan Gong, Ziren Tosi, Fabio Poggi, Matteo Mattoccia, Stefano Liu, Dong Wu, Jun |
| contents | 3D Gaussian Splatting (3DGS) has recently gained popularity in SLAM applications due to its fast rendering and high-fidelity representation. However, existing 3DGS-SLAM systems have predominantly focused on indoor environments and relied on active depth sensors, leaving a gap for large-scale outdoor applications. We present BGS-SLAM, the first binocular 3D Gaussian Splatting SLAM system designed for outdoor scenarios. Our approach uses only RGB stereo pairs without requiring LiDAR or active sensors. BGS-SLAM leverages depth estimates from pre-trained deep stereo networks to guide 3D Gaussian optimization with a multi-loss strategy enhancing both geometric consistency and visual quality. Experiments on multiple datasets demonstrate that BGS-SLAM achieves superior tracking accuracy and mapping performance compared to other 3DGS-based solutions in complex outdoor environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stereo 3D Gaussian Splatting SLAM for Outdoor Urban Scenes Li, Xiaohan Gong, Ziren Tosi, Fabio Poggi, Matteo Mattoccia, Stefano Liu, Dong Wu, Jun Robotics 3D Gaussian Splatting (3DGS) has recently gained popularity in SLAM applications due to its fast rendering and high-fidelity representation. However, existing 3DGS-SLAM systems have predominantly focused on indoor environments and relied on active depth sensors, leaving a gap for large-scale outdoor applications. We present BGS-SLAM, the first binocular 3D Gaussian Splatting SLAM system designed for outdoor scenarios. Our approach uses only RGB stereo pairs without requiring LiDAR or active sensors. BGS-SLAM leverages depth estimates from pre-trained deep stereo networks to guide 3D Gaussian optimization with a multi-loss strategy enhancing both geometric consistency and visual quality. Experiments on multiple datasets demonstrate that BGS-SLAM achieves superior tracking accuracy and mapping performance compared to other 3DGS-based solutions in complex outdoor environments. |
| title | Stereo 3D Gaussian Splatting SLAM for Outdoor Urban Scenes |
| topic | Robotics |
| url | https://arxiv.org/abs/2507.23677 |