# WideRange4D **Repository Path**: jiumao-admin/WideRange4D ## Basic Information - **Project Name**: WideRange4D - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-10-07 - **Last Updated**: 2025-10-07 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
# WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes ![WideRange4D](./figure/teaser.png) ### [Dataset Page](https://huggingface.co/datasets/Gen-Verse/WideRange4D) | [arXiv Paper](https://arxiv.org/abs/2503.13435) [Ling Yang](https://yangling0818.github.io)1*‡, [Kaixin Zhu](https://chriszkxxx.github.io)1*, [Juanxi Tian](https://tianshijing.github.io)1*, [Bohan Zeng](https://scholar.google.com/citations?user=MHo_d3YAAAAJ&hl=en)1*†, [Mingbao Lin](http://lmb.bjbxit.cn/)3, [Hongjuan Pei](https://openreview.net/profile?id=~Hongjuan_Pei1)2, [Wentao Zhang](https://zwt233.github.io)1‡, [Shuicheng Yan](http://yanshuicheng.info)3‡ 1 Peking University   2 University of the Chinese Academy of Sciences   3 National University of Singapore * Equal Contributions. † Project Leader. ‡ Corresponding Author.
## Example https://github.com/user-attachments/assets/1a31f3fa-f5a1-4cb7-aa3d-4152b6a1b49c https://github.com/user-attachments/assets/1b0e0696-fbed-4b44-8f5e-b14e48a5961d ## 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/Gen-Verse/WideRange4D cd WideRange4D git submodule update --init --recursive conda create -n WideRange4D python=3.7 conda activate WideRange4D 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 multipleviews scenes:** If you want to train 4D scene based on WideRange4D or your own dataset of multipleviews scenes, you can orginize your dataset as follows: ``` ├── data | | multipleview │ | (Our datasets name) │ | cam01 | ├── frame_00001.jpg │ ├── frame_00002.jpg │ ├── ... │ | cam02 │ ├── frame_00001.jpg │ ├── frame_00002.jpg │ ├── ... │ | ... ``` After that, you can use the `multipleviewprogress.sh` we provided to generate related data of poses and pointcloud.You can use it as follows: ```bash bash multipleviewprogress.sh (youe dataset name) ``` You need to ensure that the data folder is organized as follows after running multipleviewprogress.sh: ``` ├── data | | multipleview │ | (Our dataset name) │ | cam01 | ├── frame_00001.jpg │ ├── frame_00002.jpg │ ├── ... │ | cam02 │ ├── frame_00001.jpg │ ├── frame_00002.jpg │ ├── ... │ | ... │ | sparse_ │ ├── cameras.bin │ ├── images.bin │ ├── ... │ | points3D_multipleview.ply │ | poses_bounds_multipleview.npy ``` For other existing 4D reconstruction dataset, you can follow: For the dataset provided in [D-NeRF](https://github.com/albertpumarola/D-NeRF), you download the dataset from [dropbox](https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0). For the dataset provided in [HyperNeRF](https://github.com/google/hypernerf), 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 multipleviews scenes, you are supposed to build a configuration file named (you dataset name).py under "./arguments/mutipleview", after that, run ```python python train.py -s data/multipleview/(our dataset name) --port 6017 --expname "multipleview/(our dataset name)" --configs arguments/multipleview/(our dataset name).py ``` For your custom datasets, install nerfstudio and follow their [COLMAP](https://colmap.github.io/) pipeline. You should install COLMAP at first, then: ```python pip install nerfstudio # computing camera poses by colmap pipeline ns-process-data images --data data/your-data --output-dir data/your-ns-data cp -r data/your-ns-data/images data/your-ns-data/colmap/images python train.py -s data/your-ns-data/colmap --port 6017 --expname "custom" --configs arguments/hypernerf/default.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/(our dataset name)/" --skip_train --configs arguments/dnerf/(our dataset name).py ``` ## Evaluation You can just run the following script to evaluate the model. ``` python metrics.py --model_path "output/dnerf/(our dataset name)/" ``` ## Scripts There are some helpful scripts, please feel free to use them. `colmap.sh`: generate point clouds from input data ```bash bash colmap.sh data/hypernerf/virg/vrig-chicken hypernerf bash colmap.sh data/dynerf/sear_steak llff ``` `downsample_point.py` :downsample generated point clouds by sfm. ```python python scripts/downsample_point.py data/dynerf/sear_steak/colmap/dense/workspace/fused.ply data/dynerf/sear_steak/points3D_downsample2.ply ``` Thanks 4DGaussians, We always use `colmap.sh` to generate dense point clouds and downsample it to less than 40000 points. ## Contributions **Please feel free to raise issues or submit pull requests to contribute to our codebase.** ## Acknowledgement Some source code of ours is borrowed from [4DGaussians](https://github.com/hustvl/4DGaussians). We sincerely appreciate the excellent work of these authors. ## Citation ``` @article{yang2025widerange4d, title={WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes}, author={Yang, Ling and Zhu, Kaixin and Tian, Juanxi and Zeng, Bohan and Lin, Mingbao and Pei, Hongjuan and Zhang, Wentao and Yan, Shuichen}, journal={arXiv preprint arXiv:2503.13435}, year={2025} } ```