Compact 3D Gaussian Splatting For Dense Visual SLAM
- URL: http://arxiv.org/abs/2403.11247v1
- Date: Sun, 17 Mar 2024 15:41:35 GMT
- Title: Compact 3D Gaussian Splatting For Dense Visual SLAM
- Authors: Tianchen Deng, Yaohui Chen, Leyan Zhang, Jianfei Yang, Shenghai Yuan, Danwei Wang, Weidong Chen,
- Abstract summary: 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.
Our method achieves faster training and rendering speed while maintaining the state-of-the-art (SOTA) quality of the scene representation.
- Score: 26.47738770606461
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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 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.
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