Optimized Minimal 3D Gaussian Splatting

Joo Chan Lee, Jong Hwan Ko, and Eunbyung Park

[Project Page] [Paper(arxiv)]

Method Overview

We reduce storage requirements of 3D Gaussian Splatting while using a minimal number of primitives. First, we determine the distinct Gaussian from the near ones, minimizing redundancy without sacrificing quality. Second, we propose a compact and precise attribute representation that efficiently captures both continuity and irregularity among primitives. Additionally, we propose a sub-vector quantization technique for improved irregularity representation, maintaining fast training with a negligible codebook size.

Setup

Our code is based on Mini-Splatting.

For installation: We recommend to use cuda 12.1 with python 3.11 for easy setup.

git clone https://github.com/maincold2/OMG.git
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

Install TMC (GPCC), and add tmc3 to your environment variable or manually specify its location in the code (lines 243 and 258, this script is sourced from HAC++).

If you have trouble in installing cuml, please refer to the CUML Installation Guide.

We used Mip-NeRF 360, Tanks & Temples, and Deep Blending.

Running

Training

#for outdoor scenes (e.g., Mip-NeRF 360 outdoor and T&T scenes)
python train.py -s <path to COLMAP> -m <model path> --eval --imp_metric outdoor
#for indoor scenes (e.g., Mip-NeRF 360 indoor and DB scenes)
python train.py -s <path to COLMAP> -m <model path> --eval --imp_metric indoor

--importance_thresh

Threshold for importance-based scoring. 0.96, 0.98, 0.99, 0.999, 0.9999 for XS to XL. 0.96 (XS) by default

Both the ply file (3DGS format) and compressed file (comp.xz) are generated after traininig.

Evaluation

python render.py -s <path to COLMAP> -m <model path> --decode
python metrics.py -m <model path> 

--decode

Rendering with the compressed file (comp.xz), otherwise using the ply file. The results are the same regardless of this option.

BibTeX

@article{lee2025omg,
  author    = {Lee, Joo Chan and Ko, Jong Hwan and Park, Eunbyung},
  title     = {Optimized Minimal 3D Gaussian Splatting},
  journal   = {arXiv preprint arXiv:2503.16924},
  year      = {2025},
}