Compact 3D Gaussian Splatting For Dense Visual SLAM
Tianchen Deng Yaohui Chen Leyan Zhang Jianfei Yang Shenghai Yuan Danwei Wang Weidong Chen
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.