用几何对齐的高斯点云实现高精度实时三维重建与定位
G2S-ICP SLAM: Geometry-aware Gaussian Splatting ICP SLAM
- 以局部切平面约束的二维高斯盘建模表面，提升深度一致性
- 在ICP框架中引入各向异性协方差先验，实现稳定位姿估计
- 结合光照、深度与法向一致性损失，适合高保真场景重建
本文提出一种新型几何感知的RGB-D高斯点云SLAM系统G2S-ICP SLAM。该方法通过将每个场景元素表示为受限于局部切平面的高斯分布，以二维高斯盘形式对齐几何结构，相比传统各向同性3D椭球表示，显著提升多视角下深度解释的一致性。为集成该表示到SLAM流程，我们在广义ICP框架中引入各向异性协方差先验，不改变原有配准形式。同时提出几何感知损失，联合监督光度、深度与法向一致性。系统实现实时运行，同时保持视觉与几何保真度。在Replica和TUM-RGBD数据集上的大量实验表明，G2S-ICP SLAM在定位精度与重建完整性方面优于现有SLAM系统，且维持高质量渲染效果。
原文摘要 · Abstract (English)
In this paper, we present a novel geometry-aware RGB-D Gaussian Splatting SLAM system, named G2S-ICP SLAM. The proposed method performs high-fidelity 3D reconstruction and robust camera pose tracking in real-time by representing each scene element using a Gaussian distribution constrained to the local tangent plane. This effectively models the local surface as a 2D Gaussian disk aligned with the underlying geometry, leading to more consistent depth interpretation across multiple viewpoints compared to conventional 3D ellipsoid-based representations with isotropic uncertainty. To integrate this representation into the SLAM pipeline, we embed the surface-aligned Gaussian disks into a Generalized ICP framework by introducing anisotropic covariance prior without altering the underlying registration formulation. Furthermore we propose a geometry-aware loss that supervises photometric, depth, and normal consistency. Our system achieves real-time operation while preserving both visual and geometric fidelity. Extensive experiments on the Replica and TUM-RGBD datasets demonstrate that G2S-ICP SLAM outperforms prior SLAM systems in terms of localization accuracy, reconstruction completeness, while maintaining the rendering quality.
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