RGBD GS-ICP SLAM (100FPS Gaussian Splatting SLAM)

ECCV 2024

This repository is intended to substantiate the results reported in the paper. Additional features including visualization tools will be updated soon!

Environments

Install requirements

conda create -n gsicpslam python==3.9
conda activate gsicpslam
conda install pytorch==2.0.0 torchvision==0.15.0 torchaudio==2.0.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt

Also, PCL is needed for fast-gicp submodule.

Install submodules

conda activate gsicpslam
pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn

cd submodules/fast_gicp
mkdir build
cd build
cmake ..
make
cd ..
python setup.py install --user

Datasets

Replica

Download
```
bash download_replica.sh
```

Configure

Please modify the directory structure to ours.

The original structure

Replica
    - room0
        - results (contain rgbd images)
            - frame000000.jpg
            - depth000000.jpg
            ...
        - traj.txt
    ...

Our structure

Replica
    - room0
        - images (contain rgb images)
            - frame000000.jpg
            ...
        - depth_images (contain depth images)
            - depth000000.jpg
            ...
        - traj.txt
    ...

TUM-RGBD
- Download
```
    bash download_tum.sh
```

Run

Limited to 30 FPS

    # Replica
    bash replica.sh
    # TUM
    bash tum.sh

Unlimited tracking speed

    # Replica
    bash replica_unlimit.sh
    # TUM
    bash tum_unlimit.sh

Installing SIBR Viewer

cd SIBR_viewers
cmake -Bbuild . -DCMAKE_BUILD_TYPE=Release
cmake --build build -j24 --target install

Real-time demo

Using rerun.io viewer

Rerun viewer shows the means of trackable Gaussians, and rendered image from reconstructed 3dgs map.

python -W ignore gs_icp_slam.py --rerun_viewer

Using SIBR viewer

python -W ignore gs_icp_slam.py --dataset_path dataset/Replica/office0 --verbose

# In other terminal
cd SIBR_viewers
./install/bin/SIBR_remoteGaussian_app --rendering-size 1280 720

Docker

Please see the README.md in the docker_files folder.

tccoin/GS_ICP_SLAM