TameMini-GS: A Hybrid Approach for Efficient and Compact 3D Gaussian Splatting

This is the official implementation of TameMini-GS, a model that integrates optimization techniques from Mini-Splatting, Taming3DGS, and a coarse-to-fine training approach to effectively balance training time, rendering quality, and compact 3DGS representation. Link paper: https://ieeexplore.ieee.org/document/11142403

(1) Setup

This code has been tested with Python 3.8, torch 1.12.1, CUDA 11.6.

• Clone the repository

git clone https://github.com/trungphien/TameMini-GS.git

• Setup python environment

conda create -n tame_mini python=3.8
conda activate tame_mini
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

• Download datasets: Mip-NeRF 360, T&T+DB COLMAP.

(2) Training & Evaluation

Training scripts for TameMini-GS are in tame_mini:

cd tame_mini

• Train with train/test split:

# mipnerf360 outdoor
python train.py -s <dataset path> -m <model path> -i images_4 --eval --imp_metric outdoor --config_path ../config/fast
# mipnerf360 indoor
python train.py -s <dataset path> -m <model path> -i images_2 --eval --imp_metric indoor --config_path ../config/fast
# t&t
python train.py -s <dataset path> -m <model path> --eval --imp_metric outdoor --config_path ../config/fast
# db
python train.py -s <dataset path> -m <model path> --eval --imp_metric indoor --config_path ../config/fast

• Modified full_eval script:

python full_eval.py -m360 <mipnerf360 folder> -tat <tanks and temples folder> -db <deep blending folder>

(3) Compression

• Scripts for compression are in ms_c:

cd ms_c

• Compress, decompress, and evaluate (use the model path of the pretrained Mini-Splatting):

python run.py -s <dataset path> -m <model path>

Acknowledgement. This project is built upon Mini-Splatting, 3DGS, Taming 3DGS. and Mini-Splatting2