https://github.com/wbjang/code-nerf

Official repository for CodeNeRF
https://github.com/wbjang/code-nerf

Last synced: about 22 hours ago
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Official repository for CodeNeRF

• Host: GitHub
• URL: https://github.com/wbjang/code-nerf
• Owner: wbjang
• License: mit
• Created: 2021-08-15T11:19:05.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-02-21T16:29:27.000Z (9 months ago)
• Last Synced: 2024-02-21T17:39:38.379Z (9 months ago)
• Language: Python
• Size: 36.1 KB
• Stars: 121
• Watchers: 16
• Forks: 14
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

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README

        


## CodeNeRF: Disentangled Neural Radiance Fields for Object Categories

Date : 27th Feb, 2022

This contains the implementation of the paper [CodeNeRF](https://arxiv.org/abs/2109.01750). 

Please refer to the [project webpage](https://sites.google.com/view/wbjang/home/codenerf) for demos.

### Install the environment

```

conda env create -f environment.yml

conda activate code_nerf

```

### Catalog

- [x] Training

- [x] Optimizing with GT pose

- [ ] Editing Shapes/Textures

- [ ] Pose Optimizing

### Download the data (ShapeNet-SRN)

For ShapeNet-SRN dataset, you can download it from https://drive.google.com/drive/folders/1PsT3uKwqHHD2bEEHkIXB99AlIjtmrEiR

### Training

```

python train.py --gpu  --save_dir  --jsonfiles  --iters_crop 1000000 --iters_all 1200000

```

JSON files contain hyper-parameters as well as data directory. 'iters_crop' and 'iters_all' are number of iterations for both cropped and whole images.

### Optimizing

```

python optimize.py --gpu  --saved_dir 

```

The result will be stored in , and each folder contains the progress of optimization, and the evaluation of test set. 

The final optimized results and the quantitative evaluations are stored in 'trained_dir/test(_num)/codes.pth'

### BibTex

```

@inproceedings{jang2021codenerf,

  title={Codenerf: Disentangled neural radiance fields for object categories},

  author={Jang, Wonbong and Agapito, Lourdes},

  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},

  pages={12949--12958},

  year={2021}

}

```

### References

Some parts of code are borrowed from below amazing repositories.

* The GitHub repository of Pixel NeRF : https://github.com/sxyu/pixel-nerf

* nerf_pl : https://github.com/kwea123/nerf_pl

* NeRF-pytorch : https://github.com/yenchenlin/nerf-pytorch

### Supplementary Video

https://user-images.githubusercontent.com/32883157/130004248-0ff74d4e-993e-43f2-91ee-bd25776e65bc.mp4

### License

MIT