This repository is intended to substantiate the results reported in the paper. Additional features including visualization tools will be updated soon!
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
-
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
- Download
-
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
cd SIBR_viewers
cmake -Bbuild . -DCMAKE_BUILD_TYPE=Release
cmake --build build -j24 --target install
Rerun viewer shows the means of trackable Gaussians, and rendered image from reconstructed 3dgs map.
python -W ignore gs_icp_slam.py --rerun_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
Please see the README.md in the docker_files folder.