https://github.com/hustvl/hdr-hexplane

3DV 2024: Fast High Dynamic Range Radiance Fields for Dynamic Scenes
https://github.com/hustvl/hdr-hexplane

hdr-image nerf neuralrendering reconstruction

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3DV 2024: Fast High Dynamic Range Radiance Fields for Dynamic Scenes

• Host: GitHub
• URL: https://github.com/hustvl/hdr-hexplane
• Owner: hustvl
• License: mit
• Created: 2023-10-03T14:21:06.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-08-09T07:04:03.000Z (3 months ago)
• Last Synced: 2024-08-09T08:28:29.001Z (3 months ago)
• Topics: hdr-image, nerf, neuralrendering, reconstruction
• Language: Python
• Homepage: https://guanjunwu.github.io/HDR-HexPlane
• Size: 13.1 MB
• Stars: 26
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# HDR-HexPlane: Fast High Dynamic Range Radiance Fields for Dynamic Scenes

## 3DV 2024

### [Project Page](https://guanjunwu.github.io/HDR-HexPlane/) | [Data(BaiduYun)](https://pan.baidu.com/s/1vuJ5kThgRkmv9DWis9g8Pg?pwd=1234) | [Data(Blender Files)](https://drive.google.com/drive/folders/19eTjvgw98_hYodCMegHHg5PjhNUgFVNO?usp=sharing)| [Data(Images)](https://huggingface.co/datasets/SmallGuanjun/HDR-HexPlane) |[Paper](https://arxiv.org/abs/2401.06052) 

[Guanjun Wu](https://guanjunwu.github.io/) 1*, [Taoran Yi](https://github.com/taoranyi) 2*,

[Jiemin Fang](https://jaminfong.cn/) 2‡, [Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu/) 2, [Xinggang Wang](https://xwcv.github.io) 2‡✉

1 School of CS, HUST   2 School of EIC, HUST  

\* Equal Contributions. $\ddagger$ Project Lead. ✉ Corresponding Author.

---

We propose a dynamic HDR NeRF framework, named as HDR-HexPlane, which can learn 3D scenes from dynamic 2D images captured with various exposures. We further construct a dataset containing multiple dynamic scenes captured with diverse exposures for evaluation.

![image](docs/framework.jpg)

# Environment Setup

```

# create conda environment

conda create --name hdrhexplane python=3.9

# pip install 

pip install -r requirements.txt

```

# Data Preparation

Please download all the data from the [link](https://drive.google.com/drive/folders/19eTjvgw98_hYodCMegHHg5PjhNUgFVNO?usp=sharing):

Make the dataset format like: `dataset/lego`.

Please change the "datadir" in config based on the locations of downloaded datasets.

# Reconstruction

```

python main.py config=config/dnerf_slim_tank.yaml

```

We provide several config files under [config](config/) folder for different datasets and models.

# Evaluation

With `render_test=True`, `render_path=True`, results at test viewpoint are automatically evaluated and validation viewpoints are generated after reconstruction.

```

python main.py config=config/dnerf_slim_airplane.yaml systems.ckpt="checkpoint/path" render_only=True

```

# Citation

Some insights about neural voxel grids and dynamic scenes reconstruction originate from [TiNeuVox](https://github.com/hustvl/TiNeuVox) and [hexplane](https://github.com/Caoang327/HexPlane). If you find this repository/work helpful in your research, welcome to cite these papers and give a ⭐.

```

@inproceedings{wu2024fast,

  title={Fast High Dynamic Range Radiance Fields for Dynamic Scenes},

  author={Wu, Guanjun and Yi, Taoran and Fang, Jiemin and Liu, Wenyu and Wang, Xinggang},

  booktitle={2024 International Conference on 3D Vision (3DV)},

  pages={862--872},

  year={2024},

  organization={IEEE}

}

```

# Acknowledgement

Our code is hugely influenced by [hexplane](https://github.com/Caoang327/HexPlane) and many other projects. We would like to acknowledge them for making great code openly available for us to use. **All the datasets are provided for academic studies only.**