This repository is a fork of the official authors implementation associated with the paper "3D Gaussian Splatting for Real-Time Radiance Field Rendering".

The code for "Compact 3D Scene Representation via Self-Organizing Gaussian Grids" consists of multiple parts. The multi-dimensional sorting algorithm, PLAS, is available under the Apache License at fraunhoferhhi/PLAS.

The integration of the sorting, the smoothness regularization and the compression code for training and compressing 3D scenes is available in this repository.

The repository contains submodules, thus please check it out with

# SSH
git clone [email protected]:fraunhoferhhi/Self-Organizing-Gaussians.git --recursive

or

# HTTPS
git clone https://github.com/fraunhoferhhi/Self-Organizing-Gaussians.git --recursive

The code is using a few additional Python packages on top of graphdeco-inria/gaussian-splatting. We provide an extended environment.yml:

Installation with micromamba:

micromamba env create --file environment.yml --channel-priority flexible -y
micromamba activate sogs

Download a dataset, e.g. T&T.

The train.py script expects a name to a .yaml config file in the config/ folder. All parameters for the run are by default loaded from the yaml file. An example launch file can be found in .vscode/launch.json, for launching from Visual Studio Code.

Example:

python train.py \
  --config-name ours_q_sh_local_test \
  hydra.run.dir=/data/output/${now:%Y-%m-%d}/${now:%H-%M-%S}-${run.name} \
  dataset.source_path=/data/gaussian_splatting/tandt_db/tandt/truck \
  run.no_progress_bar=false \
  run.name=vs-code-debug

The parameter configurations can be overriden in the launch as shown (using Hydra).

Code differences can be found in this diff: https://github.com/fraunhoferhhi/Self-Organizing-Gaussians/pull/1/files

• different command-line interface for train.py (using Hydra)
• wandb.ai used for logging

• post-training quantization, compression/decompression
• xyz log activation (gaussian_model.py)
• grid sorting, neighbor loss (gaussian_model.py)
• option to disable spherical harmonics

• 2024-07-09: Project website updated with TLDR, contributions, insights and comparison to concurrent methods
• 2024-07-01: Our work was accepted at ECCV 2024 🥳
• 2024-06-13: Training code available
• 2024-05-14: Improved compression scores! New results for paper v2 available on the project website
• 2024-05-02: Revised paper v2 on arXiv: Added compression of spherical harmonics, updated compression method with improved results (all attributes compressed with JPEG XL now), added qualitative comparison of additional scenes, moved compression explanation and comparison to main paper, added comparison with "Making Gaussian Splats smaller".
• 2024-02-22: The code for the sorting algorithm is now available at fraunhoferhhi/PLAS
• 2024-02-21: Video comparisons for different scenes available on the project website
• 2023-12-19: Preprint available on arXiv