Recent work has shown that 3D Gaussian-based SLAM enables high-quality
reconstruction, accurate pose estimation, and real-time rendering of scenes.
However, these approaches are built on a tremendous number of redundant 3D
Gaussian ellipsoids, leading to high memory and storage costs, and slow
training speed. To address the limitation, we propose a compact 3D Gaussian
Splatting SLAM system that reduces the number and the parameter size of
Gaussian ellipsoids. A sliding window-based masking strategy is first proposed
to reduce the redundant ellipsoids. Then we observe that the covariance matrix
(geometry) of most 3D Gaussian ellipsoids are extremely similar, which
motivates a novel geometry codebook to compress 3D Gaussian geometric
attributes, i.e., the parameters. Robust and accurate pose estimation is
achieved by a global bundle adjustment method with reprojection loss. Extensive
experiments demonstrate that our method achieves faster training and rendering
speed while maintaining the state-of-the-art (SOTA) quality of the scene
representation.

提出了一种紧凑的 3D 高斯着色 SLAM 系统，通过减少冗余椭球体的数量和参数大小来降低内存和存储成本，并实现了快速训练和呈现速度。通过滑动窗口的屏蔽策略减少冗余的椭球体，然后观察到大多数 3D 高斯椭球体的协方差矩阵（几何）非常相似，从而激发出一种新的几何码本来压缩 3D 高斯几何属性。通过全局捆绑调整方法和重投影损失实现了稳健准确的姿态估计，实验结果表明我们的方法在保持最先进的场景表示质量的同时实现了更快的训练和呈现速度。