仅用彩色图像实现快速高精度单目场景重建，兼顾几何准确与渲染质量。
HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction
- 融合单目先验与学习型稠密SLAM提升几何估计能力
- 基于3D高斯泼溅实现高效场景建模，闭环时实时全局一致性优化
- 网格化尺度对齐策略增强深度细节，适合实时重建应用
我们提出HI-SLAM2，一种几何感知的高斯SLAM系统，仅使用RGB输入即可实现快速且高精度的单目场景重建。现有神经SLAM或基于3D高斯溅射（3DGS）的SLAM方法常在渲染质量与几何准确性之间权衡，而本研究证明二者可同时达成。核心思路是通过结合易获取的单目先验与基于学习的稠密SLAM来增强几何估计能力，并以3D高斯泼溅作为核心地图表示，高效建模场景。在回环检测后，通过高效的位姿图捆绑调整实现即时全局一致性，同时基于锚定关键帧更新显式变形3D高斯单元，实现瞬时地图更新。此外，引入基于网格的尺度对齐策略，维持先验深度的更优尺度一致性，以获得更精细的深度细节。在Replica、ScanNet和ScanNet++数据集上的大量实验表明，该方法显著优于现有神经SLAM方法，甚至在重建与渲染质量上超越了部分基于RGB-D的方法。项目页面与源代码将发布于https://hi-slam2.github.io/。
原文摘要 · Abstract (English)
We present HI-SLAM2, a geometry-aware Gaussian SLAM system that achieves fast and accurate monocular scene reconstruction using only RGB input. Existing Neural SLAM or 3DGS-based SLAM methods often trade off between rendering quality and geometry accuracy, our research demonstrates that both can be achieved simultaneously with RGB input alone. The key idea of our approach is to enhance the ability for geometry estimation by combining easy-to-obtain monocular priors with learning-based dense SLAM, and then using 3D Gaussian splatting as our core map representation to efficiently model the scene. Upon loop closure, our method ensures on-the-fly global consistency through efficient pose graph bundle adjustment and instant map updates by explicitly deforming the 3D Gaussian units based on anchored keyframe updates. Furthermore, we introduce a grid-based scale alignment strategy to maintain improved scale consistency in prior depths for finer depth details. Through extensive experiments on Replica, ScanNet, and ScanNet++, we demonstrate significant improvements over existing Neural SLAM methods and even surpass RGB-D-based methods in both reconstruction and rendering quality. The project page and source code will be made available at https://hi-slam2.github.io/.
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