This is the official implementation of Mini-Splatting2, a point cloud reconstruction work in the context of Gaussian Splatting. Through aggressive Gaussian densification, our algorithm enables fast scene optimization within minutes. For technical details, please refer to:
Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification
Guangchi Fang and Bing Wang.
[Paper]
This code has been tested with Python 3.8, torch 1.12.1, CUDA 11.6.
- Clone the repository
git clone git@github.com:fatPeter/mini-splatting2.git && cd mini-splatting2 - Setup python environment
conda create -n mini_splatting2 python=3.8 conda activate mini_splatting2 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.
Training scripts for Mini-Splatting2 are in msv2:
cd msv2 - 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> Training scripts for Mini-Splatting2-D are in msv2_d:
cd msv2_d - 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> This implementation directly support dense point cloud reconstruction:
# similar to train.py (-i images_4/images_2, --imp_metric outdoor/indoor) # output ply files are saved in ./teaser python teaser.py -s <dataset path> -m <model path> -i images_4 --eval --imp_metric outdoor Acknowledgement. This project is built upon Mini-Splatting, 3DGS and Taming 3DGS.