# Stereo 3D Gaussian Splatting SLAM for Outdoor Urban Scenes

arXiv: https://arxiv.org/abs/2507.23677 · HTML: https://mlsanity.com/paper/2507.23677/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [MatchAttention: Embedding Explicit Matching Constraints into Attention for Efficient Stereo Matching](https://mlsanity.com/paper/2510.14260/) (2025)
- [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026)
- [MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos](https://mlsanity.com/paper/2607.15211/) (2026)

## Abstract (arXiv)

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.