https://github.com/againstentropy/netf
https://github.com/againstentropy/netf
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/againstentropy/netf
- Owner: AgainstEntropy
- License: mit
- Created: 2022-03-10T06:08:23.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-03-10T06:26:08.000Z (over 4 years ago)
- Last Synced: 2025-02-12T09:57:45.995Z (over 1 year ago)
- Language: Python
- Size: 58.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NeTF_public
The repository is the source code for paper "Non-line-of-Sight Imaging via Neural Transient Fields". [[Paper]](https://arxiv.org/abs/2101.00373#:~:text=Title%3ANon-line-of-Sight%20Imaging%20via%20Neural%20Transient%20Fields.%20Non-line-of-Sight%20Imaging,within%20a%20pre-defined%20volume%29%20of%20the%20hidden%20scene.)
The preprocessed data we use can be downloaded at [[Google Drive]](https://drive.google.com/file/d/1kGVrFcNZZbZs0ute_roEOg5UkYeh3jRl/view?usp=sharing) or [[Baidu Netdisk]](https://pan.baidu.com/s/16lWXwhm8CbXWAumJmlw9MQ) with password: netf
The raw data can be downloaded at [Zaragoza NLOS synthetic dataset](https://graphics.unizar.es/nlos_dataset.html), [f-k migration](http://www.computationalimaging.org/publications/nlos-fk/) and [Convolutional Approximations](https://imaging.cs.cmu.edu/conv_nlos/)
We also provide MATLAB code 'zaragoza_preprocess.m' and 'fkdata_preprocess.m' to convert data from Zaragoza dataset and fk to fit NeTF for those who want to run NeTF at other scene.
# Environment setup
Make sure that the dependcies in `requirements.txt` are installed, or they can be installed by
```
"pip install -r requirements.txt"
```
# How to run
Make sure that data is place correctly like
```
NeTF_public
│ README.md
│ run_netf.py
│ ...
│
└───data
│ fk_dragon_meas_180_min_256_preprocessed.mat
│ ...
│
└───zaragozadataset
│ zaragoza256_preprocessed.mat
│ ...
```
Then run with preset settings:
```
"python run_netf.py --config configs/zaragoza_bunny.txt"
```
Different settings are stroaged at "./configs/".
Under preset settings, the training process takes around 24 hours on a single NVIDIA Tesla M40 GPU.
# Results
The final volume and slices from different view are stroaged at "./model"
The matlab script "show_result.m" is also provided to generate 2D images from different views and 3D density distribution.
And the comparision between predicted and measured histogram is stroaged at "./figure"
# Contact us
Please email shensy@shanghaitech.edu.cn or wangzi@shanghaitech.edu.cn if you have any questions or suggestions.