A real-time Gaussian Splatting implementation for SLAM systems, building upon the excellent work of MonoGS and Photo-SLAM.
git clone https://github.com/Nemo0412/MonoRTGS.git
cd MonoRTGSEnvironment Note
environment.ymlis for x86 architecture.environment_arch.ymlis for arm64 architecture (e.g., ONX).
conda env create -f environment.yml
conda activate MonoRTGSThe following scripts will automatically download datasets into the ./data folder.
bash scripts/download_tum.shbash scripts/download_replica.shThis project includes convenient scripts to run both the original version (MonoGS) and RTGS version:
python3 run_slam.py <version> <config>./run_slam.sh <version> <config># Run original version
python3 run_slam.py original tum/fr3_office
./run_slam.sh original tum/fr3_office
# Run RTGS version
python3 run_slam.py RTGS tum/fr3_office
./run_slam.sh RTGS tum/fr3_officetum/fr1_desktum/fr2_xyztum/fr3_office
replica/office0replica/office1replica/office2replica/office3replica/office4replica/room0replica/room1replica/room2
RTGS/
├── run_slam.py # Python version SLAM runner script
├── run_slam.sh # Bash version SLAM runner script
├── MonoGS/ # Original version
├── MonoGS_RTGS/ # RTGS version
├── configs/ # Configuration files
├── scripts/ # Download scripts
└── data/ # Dataset directory
You can also run this project directly using Docker, without manual environment setup.
docker pull mugen0412/monortgs:cuda12.1
docker run --rm -it --gpus all mugen0412/monortgs:cuda12.1 bashFor a RTGS-SLAM implementation based on Photo-SLAM, please check out:
This project builds upon the excellent work of the authors of MonoGS and Photo-SLAM.
We gratefully acknowledge their open-source contributions, which make this project possible.