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inuex35 / 360-gaussian-splatting

This repository contains programs for reconstructing 3D space using OpenSfM and Gaussian Splatting techniques. It allows users to generate point clouds from images captured by a 360-degree camera using OpenSfM, and then train Gaussian Splatting models using the generated point clouds.
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360 Gaussian Splatting

360 gaussian splatting with spherical render

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.

Support me

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.

Environment Setup

Cloning the Repository

Clone the repository with the following command:

git clone --recursive https://github.com/inuex35/360-gaussian-splatting

Creating the Environment

In addition to the original repository, install the following module as well:

pip3 install submodules/diff-gaussian-rasterization submodules/simple-knn plyfile pyproj

Training 360 Gaussian Splatting

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. image

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.

Training parameter

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.