This repository contains programs for reconstructing space using OpenSfM and Gaussian Splatting. For original repositories of OpenSfM and Gaussian Splatting, please refer to the links provided.
This is just my personal project. If you've enjoyed using this project and found it helpful, I'd be incredibly grateful if you could chip in a few bucks to help cover the costs of running the GPU server. You can easily do this by buying me a coffee at https://www.buymeacoffee.com/inuex35.
Clone the repository with the following command:
git clone --recursive https://github.com/inuex35/360-gaussian-splatting
In addition to the original repository, install the following module as well:
pip3 install submodules/diff-gaussian-rasterization submodules/simple-knn plyfile pyproj
You can use omnigs implementation. Checkout diff-gaussian-rasterization to omnigs branch.
cd submodules/diff-gaussian-rasterization
git checkout omnigs
If you use depth and normal for training, use depth_normal_render
git clone --recursive -b depth_normal_render https://github.com/inuex35/360-gaussian-splatting
and
cd 360-gaussian-splatting
git clone https://github.com/inuex35/360-dn-diff-gaussian-rasterization submodules/360-dn-diff-gaussian-rasterization
pip3 install submodules/360-dn-diff-gaussian-rasterization submodules/simple-knn plyfile pyproj openexr imageio
First, generate point clouds using images from a 360-degree camera with OpenSfM. Refer to the following repository and use this command for reconstruction: Visit https://github.com/inuex35/ind-bermuda-opensfm and opensfm documentation for more detail.
bin/opensfm_run_all your_data
Make sure the camera model is set to spherical. It is possible to use both spherical and perspective camera models simultaneously.
After reconstruction, a reconstruction.json
file will be generated. You can use opensfm viewer for visualization.
Assuming you are creating directories within data
, place them as follows:
data/your_data/images/*jpg
data/your_data/reconstruction.json
Then, start the training with the following command:
python3 train.py -s data/your_data --panorama
After training, results will be saved in the output
directory. For training parameters and more details, refer to the Gaussian Splatting repository.
Parameters for 360 Gaussian Splatting are provided with default values in 360-gaussian-splatting/arguments/init.py.
According to the original repository, it might be beneficial to adjust position_lr_init, position_lr_final, and scaling_lr.
Reducing densify_grad_threshold can increase the number of splats, but it will also increase VRAM usage.
densify_from_iter and densify_until_iter are also related to densification.
You should use small densify_grad_threshold like 0.00002 for equirectangular.