Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20854 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912505584943104 |
|---|---|
| author | Fan, Ruoyu Wen, Yuhui Dai, Jiajia Zhang, Tao Zeng, Long Liu, Yong-jin |
| author_facet | Fan, Ruoyu Wen, Yuhui Dai, Jiajia Zhang, Tao Zeng, Long Liu, Yong-jin |
| contents | We propose $S^3$LAM, a novel RGB-D SLAM system that leverages 2D surfel splatting to achieve highly accurate geometric representations for simultaneous tracking and mapping. Unlike existing 3DGS-based SLAM approaches that rely on 3D Gaussian ellipsoids, we utilize 2D Gaussian surfels as primitives for more efficient scene representation. By focusing on the surfaces of objects in the scene, this design enables $S^3$LAM to reconstruct high-quality geometry, benefiting both mapping and tracking. To address inherent SLAM challenges including real-time optimization under limited viewpoints, we introduce a novel adaptive surface rendering strategy that improves mapping accuracy while maintaining computational efficiency. We further derive camera pose Jacobians directly from 2D surfel splatting formulation, highlighting the importance of our geometrically accurate representation that improves tracking convergence. Extensive experiments on both synthetic and real-world datasets validate that $S^3$LAM achieves state-of-the-art performance. Code will be made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $S^3$LAM: Surfel Splatting SLAM for Geometrically Accurate Tracking and Mapping Fan, Ruoyu Wen, Yuhui Dai, Jiajia Zhang, Tao Zeng, Long Liu, Yong-jin Computer Vision and Pattern Recognition We propose $S^3$LAM, a novel RGB-D SLAM system that leverages 2D surfel splatting to achieve highly accurate geometric representations for simultaneous tracking and mapping. Unlike existing 3DGS-based SLAM approaches that rely on 3D Gaussian ellipsoids, we utilize 2D Gaussian surfels as primitives for more efficient scene representation. By focusing on the surfaces of objects in the scene, this design enables $S^3$LAM to reconstruct high-quality geometry, benefiting both mapping and tracking. To address inherent SLAM challenges including real-time optimization under limited viewpoints, we introduce a novel adaptive surface rendering strategy that improves mapping accuracy while maintaining computational efficiency. We further derive camera pose Jacobians directly from 2D surfel splatting formulation, highlighting the importance of our geometrically accurate representation that improves tracking convergence. Extensive experiments on both synthetic and real-world datasets validate that $S^3$LAM achieves state-of-the-art performance. Code will be made publicly available. |
| title | $S^3$LAM: Surfel Splatting SLAM for Geometrically Accurate Tracking and Mapping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.20854 |