ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911356782903296 |
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| author | Zhang, Ganlin Qian, Shenhan Wang, Xi Cremers, Daniel |
| author_facet | Zhang, Ganlin Qian, Shenhan Wang, Xi Cremers, Daniel |
| contents | We present ViSTA-SLAM as a real-time monocular visual SLAM system that operates without requiring camera intrinsics, making it broadly applicable across diverse camera setups. At its core, the system employs a lightweight symmetric two-view association (STA) model as the frontend, which simultaneously estimates relative camera poses and regresses local pointmaps from only two RGB images. This design reduces model complexity significantly, the size of our frontend is only 35\% that of comparable state-of-the-art methods, while enhancing the quality of two-view constraints used in the pipeline. In the backend, we construct a specially designed Sim(3) pose graph that incorporates loop closures to address accumulated drift. Extensive experiments demonstrate that our approach achieves superior performance in both camera tracking and dense 3D reconstruction quality compared to current methods. Github repository: https://github.com/zhangganlin/vista-slam |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01584 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association Zhang, Ganlin Qian, Shenhan Wang, Xi Cremers, Daniel Computer Vision and Pattern Recognition We present ViSTA-SLAM as a real-time monocular visual SLAM system that operates without requiring camera intrinsics, making it broadly applicable across diverse camera setups. At its core, the system employs a lightweight symmetric two-view association (STA) model as the frontend, which simultaneously estimates relative camera poses and regresses local pointmaps from only two RGB images. This design reduces model complexity significantly, the size of our frontend is only 35\% that of comparable state-of-the-art methods, while enhancing the quality of two-view constraints used in the pipeline. In the backend, we construct a specially designed Sim(3) pose graph that incorporates loop closures to address accumulated drift. Extensive experiments demonstrate that our approach achieves superior performance in both camera tracking and dense 3D reconstruction quality compared to current methods. Github repository: https://github.com/zhangganlin/vista-slam |
| title | ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.01584 |