Optimized Minimal 4D Gaussian Splatting
Minseo Lee*, Byeonghyeon Lee*, Lucas Yunkyu Lee, Eunsoo Lee, Sangmin Kim, Seunghyeon Song, Joo Chan Lee, Jong Hwan Ko, Jaesik Park, and Eunbyung Park†
Our code is built based on 4D-GS
Setup
We ran the experiments in the following environment:
- ubuntu: 20.04
- python: 3.11
- cuda: 12.1
- pytorch: 2.5.1 ( > 2.5.0 is required for svq)
- GPU: RTX 3090
1. Installation
conda create -n OMG4 python=3.11
conda activate OMG4
pip install -r requirement.txt
Then, please download the pretrained 4D-GS weight and gradients.
You can download the weights from Google Drive.
2. Training
Gradient (2D mean, t) should be calculated in advance to sample important Gaussians. If --grad is not designated, it will automatically compute gradients. Once you compute gradients (or download provided gradients), please set --grad to your gradient path, not to compute them repeatedly.
python train.py \
--config ./configs/dynerf/cook_spinach.yaml \
--start_checkpoint PATH_TO_4DGS_PRETRAINED \
--grad PATH_TO_GRADIENT \
--out_path ./cook_spinach_comp
You can check the result (w/ various metrics, encoded model size, etc.) at ./res.txt
3. Evaluation
At the end of training, the evaluation process is implemented. Or you can evaluate the trained model with the encoded "comp.xz" file with the following command
python test.py \
--config ./configs/dynerf/cook_spinach.yaml \
--comp_checkpoint ./cook_spinach_comp/comp.xz
The weights reported in our paper are available for download on Google Drive.