CGS-SLAM: Compact 3D Gaussian Splatting for Dense Visual SLAM

Abstract

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 this 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, a novel geometry codebook-based quantization method is proposed to further compress 3D Gaussian geometric attributes. Robust and accurate pose estimation is achieved by a local-to-global bundle adjustment method with reprojection loss. Extensive experiments demonstrate that our method achieves faster training, rendering speed, and low memory usage while maintaining the state-of-the-art (SOTA) quality of the scene representation.