CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field
[ECCV 2024]

Jiarui Hu^1† · Xianhao Chen^2† · Boyin Feng¹ · Guanglin Li¹ · Liangjing Yang²
Hujun Bao¹ · Guofeng Zhang¹ · Zhaopeng Cui^1*
1 State Key Lab of CAD&CG, Zhejiang University
² ZJU-UIUC Institute, International Campus, Zhejiang University
^* Corresponding author. ^†Equal contribution.

🌐Project page | 📝Paper | 📽️Video

This is the official implementation of CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field. CG-SLAM can achieve state-of-the-art performance in tracking, mapping, rendering, and efficiency.

Table of Contents

1. Update
2. Submodule
3. Installation
4. Usage
   1. Run
   2. Evaluation
5. Acknowledgement
6. Citation

Update

• Code for Diff-rasterization(w/pose --> 4✖️4 Transformation Matrix T)
• Our paper is accepted by ECCV 2024, and our code is coming soon!!!
• Code for RGBD-SLAM
• Code for Evaluation

Submodule

We have proposed a comprehensive mathematical theory on derivatives w.r.t. pose in 3D Gaussian splatting framework. Additionally, we have developed a specialized CUDA framework tailored for the SLAM task, decoupling the tracking and mapping components. For more details, please refer to the provided diff-gaussian-rasterization.

Installation

Usage

Run

Evaluation

Acknowledgement

We sincerely thank the author of the 3D Gaussian Splatting and Diff-Gaussian Rasterization repositories for their valuable contributions. Their exceptional work has been instrumental in advancing our project.

Citation

@article{hu2024cg,
    title={CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field},
    author={Hu, Jiarui and Chen, Xianhao and Feng, Boyin and Li, Guanglin and Yang, Liangjing and Bao, Hujun and Zhang, Guofeng and Cui, Zhaopeng},
    journal={arXiv preprint arXiv:2403.16095},
    year={2024}
}