Update README.md

README.md

@@ -16,103 +16,4 @@
 
 ---
 
-## Environmental Setups
-Please follow the [3D-GS](https://github.com/graphdeco-inria/gaussian-splatting) to install the relative packages.
-```bash
-git clone https://github.com/hustvl/4DGaussians
-cd 4DGaussians
-git submodule update --init --recursive
-conda create -n Gaussians4D python=3.7 
-conda activate Gaussians4D
-
-pip install -r requirements.txt
-pip install -e submodules/depth-diff-gaussian-rasterization
-pip install -e submodules/simple-knn
-```
-In our environment, we use pytorch=1.13.1+cu116.
-## Data Preparation
-**For synthetic scenes:**  
-The dataset provided in [D-NeRF](https://github.com/albertpumarola/D-NeRF) is used. You can download the dataset from [dropbox](https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0).
-
-**For real dynamic scenes:**  
-The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used. You can download scenes from [Hypernerf Dataset](https://github.com/google/hypernerf/releases/tag/v0.1) and organize them as [Nerfies](https://github.com/google/nerfies#datasets). Meanwhile, [Plenoptic Dataset](https://github.com/facebookresearch/Neural_3D_Video) could be downloaded from their official websites. To save the memory, you should extract the frames of each video and then organize your dataset as follows.
-```
-├── data
-│   | dnerf 
-│     ├── mutant
-│     ├── standup 
-│     ├── ...
-│   | hypernerf
-│     ├── interp
-│     ├── misc
-│     ├── virg
-│   | dynerf
-│     ├── cook_spinach
-│       ├── cam00
-│           ├── images
-│               ├── 0000.png
-│               ├── 0001.png
-│               ├── 0002.png
-│               ├── ...
-│       ├── cam01
-│           ├── images
-│               ├── 0000.png
-│               ├── 0001.png
-│               ├── ...
-│     ├── cut_roasted_beef
-|     ├── ...
-```
-
-
-## Training
-For training synthetic scenes such as `bouncingballs`, run 
-``` 
-python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py 
-``` 
-You can customize your training config through the config files.
-## Rendering
-Run the following script to render the images.  
-
-```
-python render.py --model_path "output/dnerf/bouncingballs/"  --skip_train --configs arguments/dnerf/bouncingballs.py  &
-```
-
-
-## Evaluation
-You can just run the following script to evaluate the model.  
-
-```
-python metrics.py --model_path "output/dnerf/bouncingballs/" 
-```
-## Scripts
-
-There are some helpful scripts in `scripts/`, please feel free to use them.
-
----
-## Contributions
-
-**This project is still under development. Please feel free to raise issues or submit pull requests to contribute to our codebase.**
-
----
-Some source code of ours is borrowed from [3DGS](https://github.com/graphdeco-inria/gaussian-splatting), [k-planes](https://github.com/Giodiro/kplanes_nerfstudio),[HexPlane](https://github.com/Caoang327/HexPlane), [TiNeuVox](https://github.com/hustvl/TiNeuVox). We sincerely appreciate the excellent works of these authors.
-
-## Acknowledgement
-
-We would like to express our sincere gratitude to [@zhouzhenghong-gt](https://github.com/zhouzhenghong-gt/) for his revisions to our code and discussions on the content of our paper.
-## Citation
-Some insights about neural voxel grids and dynamic scenes reconstruction originate from [TiNeuVox](https://github.com/hustvl/TiNeuVox). If you find this repository/work helpful in your research, welcome to cite these papers and give a ⭐. 
-```
-@article{wu20234dgaussians,
-  title={4D Gaussian Splatting for Real-Time Dynamic Scene Rendering},
-  author={Wu, Guanjun and Yi, Taoran and Fang, Jiemin and Xie, Lingxi and Zhang, Xiaopeng and Wei Wei and Liu, Wenyu and Tian, Qi and Wang Xinggang},
-  journal={arXiv preprint arXiv:2310.08528},
-  year={2023}
-}
-
-@inproceedings{TiNeuVox,
-  author = {Fang, Jiemin and Yi, Taoran and Wang, Xinggang and Xie, Lingxi and Zhang, Xiaopeng and Liu, Wenyu and Nie\ss{}ner, Matthias and Tian, Qi},
-  title = {Fast Dynamic Radiance Fields with Time-Aware Neural Voxels},
-  year = {2022},
-  booktitle = {SIGGRAPH Asia 2022 Conference Papers}
-}
-```
+## This repo has been transferred to [Here](https://github.com/CUHK-AIM-Group/EndoGaussian).