Wild Gaussian Splatting

This project combines the capabilities of DUSt3R and MASt3R for camera parameter estimation and initial point cloud creation with the 3D scene representation efficiency of Gaussian Splatting. The goal is to simplify the process of 3D scene reconstruction and visualization from images without requiring pre-set camera information or specific viewpoint data. You can perform camera estimation and initial point cloud creation using either 00_dust3r_inference.ipynb or 00_mast3r_inference.ipynb. MASt3R requires fewer resources and can handle longer sequences. Follow this with Gaussian splatting using 01_gaussian_splatting_fitting.ipynb.

Cloning the Repository

To begin, clone the repository and initialize its submodules:

git clone git@github.com:nerlfield/wild-gaussian-splatting.git --recursive
cd wild-gaussian-splatting
git pull --recurse-submodules
git submodule update --init --recursive

Create conda environment:

conda create -n wildgaussians python=3.11 cmake=3.14.0 -y
conda activate wildgaussians

Setting Up the Environment:

With the environment activated, run the provided setup script and pass your CUDA version as an argument. This script installs necessary dependencies tailored to your CUDA version:

./setup_environment.sh <cuda_version>

Replace with the version of CUDA you intend to use (e.g., 10.2, 11.1, 12.1). I used 12.1.

Starting Jupyter Lab:

This repository contains two notebooks that showcase fitting gaussians over scene from the wild:

1. ./notebooks/00_dust3r_inference.ipynb - Runs DUSt3R on a folder of images, saving camera parameters and point clouds in COLMAP format. Note: DUSt3R's memory usage increases quadratically with the number of images. On an L4 instance, it can process up to 32 images of 512x384 size.
2. ./notebooks/00_mast3r_inference.ipynb - Runs MASt3R on a folder of images, saving camera parameters and point clouds in COLMAP format.
3. ./notebooks/01_gaussian_splatting_fitting.ipynb - Reads DUSt3R or MASt3R results and applies Gaussian splatting. Modifications include the addition of rendering camera trajectory generation and bug fixes.

To launch Jupyter Lab, use:

jupyter lab --no-browser --ip 0.0.0.0 --port 4546 --allow-root --notebook-dir=.

Citing This Repository

If you use Wild Gaussian Splatting in your research, please consider citing it as follows:

@misc{wild_gaussian_splatting_2024,
  title={Wild Gaussian Splatting},
  author={Daniel Kovalenko, Ostap Hembara},
  year={2024},
  publisher={GitHub},
  howpublished={\url{https://github.com/nerlfield/wild-gaussian-splatting}}
}