仅用摄像头实现户外大场景高精度三维重建与跟踪
RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes
- 用点图替代深度图，提升相机位姿估计稳定性
- 端到端优化位姿与3D高斯点云，追踪误差降为原方法的9.8%
- 适合无深度传感器的户外自动驾驶场景应用
3D高斯点云（3DGS）已成为SLAM中的热门方案，能生成高质量新视角图像。但现有基于3DGS的方法主要针对室内场景，依赖RGB-D传感器或预训练深度模型，在户外表现不佳。为此，我们提出一种仅使用RGB图像的3DGS SLAM方法——OpenGS-SLAM，适用于无限范围的户外场景。技术上，首先采用点图回归网络在帧间生成一致的点图用于位姿估计。相比常用的深度图，点图包含多视角间的空间关系和场景几何信息，增强了位姿估计鲁棒性。随后，将估计的相机位姿与3DGS渲染结合，构建端到端可微管道，实现相机位姿与3DGS场景参数的联合优化，显著提升系统追踪精度。特别地，设计自适应尺度映射器增强点图到3DGS表示的映射准确性。在Waymo数据集上的实验表明，OpenGS-SLAM将追踪误差降至先前3DGS方法的9.8%，并在新视角合成任务中达到当前最优效果。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has become a popular solution in SLAM, as it can produce high-fidelity novel views. However, previous GS-based methods primarily target indoor scenes and rely on RGB-D sensors or pre-trained depth estimation models, hence underperforming in outdoor scenarios. To address this issue, we propose a RGB-only gaussian splatting SLAM method for unbounded outdoor scenes--OpenGS-SLAM. Technically, we first employ a pointmap regression network to generate consistent pointmaps between frames for pose estimation. Compared to commonly used depth maps, pointmaps include spatial relationships and scene geometry across multiple views, enabling robust camera pose estimation. Then, we propose integrating the estimated camera poses with 3DGS rendering as an end-to-end differentiable pipeline. Our method achieves simultaneous optimization of camera poses and 3DGS scene parameters, significantly enhancing system tracking accuracy. Specifically, we also design an adaptive scale mapper for the pointmap regression network, which provides more accurate pointmap mapping to the 3DGS map representation. Our experiments on the Waymo dataset demonstrate that OpenGS-SLAM reduces tracking error to 9.8\% of previous 3DGS methods, and achieves state-of-the-art results in novel view synthesis. Project Page: https://3dagentworld.github.io/opengs-slam/
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