NeRFocus

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Extending Mip-NeRF for rendering defocus effects

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최종 버전 다운로드 (.zip)

- blender.gin
- blender_batch2048.gin
- llff.gin
- llff_batch2048.gin
- - checkpoint_500000
  - checkpoint_610000
- - datasets.cpython-38.pyc
  - math.cpython-38.pyc
  - mip.cpython-38.pyc
  - models.cpython-38.pyc
  - utils.cpython-38.pyc
- datasets.py
- math.py
- math_test.py
- mip.py
- models.py
- utils.py
- vis.py
datatool.py
eval.py
eval_vid.py
LICENSE
README.md
requirements.txt.txt
run_nerf_helpers.py
train.py

// repository documentation

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# NeRFocus: Neural Radiance Field for 3D Synthetic Defocus
This repository contains the code release for ***[NeRFocus: Neural Radiance Field for 3D Synthetic Defocus](https://arxiv.org/abs/2203.05189)***. This implementation is written in JAX, and is a fork of Jon Barron's Mip-NeRF implementation. 
![image](https://user-images.githubusercontent.com/95485229/157234315-3ea60023-3765-40e0-b820-87653dcbcde1.png)
## Installation

`git clone https://github.com/wyhuai/nerfocus.git; cd nerfocus`  
`conda install pip; pip install --upgrade pip`  
`pip install -r requirements.txt`

## Evaluation
We provide a pretrained model in experiments/horns, so you can run the following command to generate a video with defocus effects. You may change the lens parameters "l" and "a" in eval_vid.py to adjust the focus distance and aperture size. 
`python -m eval_vid --data_dir=horns --train_dir=experiments/horns --chunk=3196 --gin_file=configs/llff.gin --logtostderr`

## Data
You can download the datasets from the [NeRF official Google Drive](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1).

### Generate multi-blur datasets
You can generate the multi-blur datasets by running datatool.py, remember to change your desired data path and the blur kernel size.

## Training
Run the following command, make sure the path is correct. You also need to change the path inside train.py to your data path.  
`python -m train --data_dir=horns --train_dir=experiments/horns --gin_file=configs/llff.gin --logtostderr`

You can also train your own dataset, as long as it confroms to NeRF data format.

## Results
[Click to watch video demonstration](https://www.bilibili.com/video/BV1ZV4y1x7mj?spm_id_from=333.999.0.0&vd_source=76ca20f72a423dd9323e2492733dffa5)

![image](https://user-images.githubusercontent.com/95485229/157253266-c9c70953-9a7e-4f84-b10a-e5d1dbccdb95.png)
![image](https://user-images.githubusercontent.com/95485229/157253365-d5d371f0-192b-4ea8-9ed6-7364848ea767.png)
![image](https://user-images.githubusercontent.com/95485229/157254773-1d30b1de-27f5-4b82-b106-024698255c36.png)

NeRFocus

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