Awesome
E-D3DGS : Embedding-Based Deformable 3D Gaussian Splatting (ECCV 2024)
Jeongmin Bae<sup>1*</sup>, Seoha Kim<sup>1*</sup>, Youngsik Yun<sup>1</sup>, </br> Hahyun Lee<sup>2 </sup>, Gun Bang<sup>2</sup>, Youngjung Uh<sup>1†</sup>
<sup>1</sup>Yonsei University <sup>2</sup>Electronics and Telecommunications Research Institute (ETRI) <br><sup>*</sup> Equal Contributions <sup>†</sup> Corresponding Author
Official repository for <a href="https://arxiv.org/abs/2404.03613">"Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting"</a><be>. <br> Our approach employs per-Gaussian latent embeddings to predict deformation for each Gaussian and achieves a clearer representation of dynamic motion. <br> We uploaded the checkpoints, configs, and rendered videos for paper results here.
Environmental Setup
Please follow the 3DGS to install the relative packages.
git clone https://github.com/JeongminB/E-D3DGS.git
cd E-D3DGS
git submodule update --init --recursive
conda create -n ed3dgs python=3.7
conda activate ed3dgs
# If submodules fail to be downloaded, refer to the repository of 3DGS
pip install -r requirements.txt
pip install -e submodules/diff-gaussian-rasterization/
pip install -e submodules/simple-knn/
We use pytorch=1.13.1+cu116
in our environment.
Data Preparation
Downloading Datasets:
Please download datasets from their official websites : HyperNerf, Neural 3D Video and Technicolor <br><br>
- Please remove 'cam13.mp4' and corresponding pose from <i>coffee_martini</i> scene in the Neural 3D Video dataset. <br>
- We split the entire <i>flame_salmon_1_split</i> scene into four 300-frame scenes.
Extracting point clouds from COLMAP:
# setup COLMAP
bash script/colmap_setup.sh
conda activate colmapenv
# automatically extract the frames and reorginize them
python script/pre_n3v.py --videopath <dataset>/<scene>
python script/pre_technicolor.py --videopath <dataset>/<scene>
python script/pre_hypernerf.py --videopath <dataset>/<scene>
# downsample dense point clouds
python script/downsample_point.py \
<location>/<scene>/colmap/dense/workspace/fused.ply <location>/<scene>/points3D_downsample.ply
After running COLMAP, Neural 3D Video and Technicolor datasets are orginized as follows:
├── data
│ | n3v
│ ├── cook_spinach
│ ├── colmap
│ ├── images
│ ├── cam01
│ ├── 0000.png
│ ├── 0001.png
│ ├── ...
│ ├── cam02
│ ├── 0000.png
│ ├── 0001.png
│ ├── ...
│ ├── cut_roasted_beef
| ├── ...
Training
To resize the training image, modify -r 2
in the command line.
# Train
python train.py -s $GT_PATH/$SCENE --configs arguments/$DATASET/$CONFIG.py --model_path $OUTPUT_PATH --expname $DATASET/$SCENE -r 2
Rendering
# Render test view only
python render.py --model_path $OUTPUT_PATH --configs arguments/$DATASET/$CONFIG.py --skip_train --skip_video
# Render train view, test view, and spiral path
python render.py --model_path $OUTPUT_PATH --configs arguments/$DATASET/$CONFIG.py
Evaluation
Note: In our paper, we calculate FPS by measuring rendering time only (except for save_image, etc.).
# Evaluate
python metrics.py --model_path $SAVE_PATH/$DATASET/$CONFIG
Note
- We provide scripts that collectively perform training, rendering, and evaluation. See the
train_<dataset_name>.sh
. - You will need to configure the dataset path according to your system.
- In the config file, make sure that the
total_num_frames
andmaxtime
are equal to the total number of training frames.
Acknowledgements
This code is based on 3DGS, 4DGaussians and STG. In particular, we used 4DGaussians as a starting point for our study. We would like to thank the authors of these papers for their hard work. 😊
BibTex
@inproceedings{bae2024ed3dgs,
title={Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting},
author={Bae, Jeongmin and Kim, Seoha and Yun, Youngsik and Lee, Hahyun and Bang, Gun and Uh, Youngjung},
booktitle = {European Conference on Computer Vision (ECCV)},
year={2024}
}