Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)
Ruixu Geng, Yang Hu, Zhi Lu, Cong Yu, Houqiang Li, Hengyu Zhang and Yan Chen, “Passive Non-Line-of-Sight Imaging Using Optimal Transport,” IEEE Transactions on Image Processing, 2021
@article{nlosot,
title = {Passive Non-Line-of-Sight Imaging Using Optimal Transport},
journal = {IEEE Transactions on Image Processing},
author = {Geng, Ruixu and Hu, Yang and Lu, Zhi and Yu, Cong and Li, Houqiang and Zhang, Hengyu and Chen, Yan},
year = {2021}
}
In this repository, we release the NLOS-OT code in Pytorch as well as the passive NLOS imaging dataset NLOS-Passive.
- Problem statement: Passive NLOS imaging
- NLOS-OT architecture
- The reconstruction results of NLOS-OT trained by specific dataset without partial occluder
- The generalization results of NLOS-OT trained by dataset only from STL-10 with unknown partial occluder
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install required packages
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clone the repo
- Download dataset
You can download each group in NLOS-Passive through the link below. Please note that a compressed package (.zip or .z01+.zip) represents a group of measured data.
link：https://pan.baidu.com/s/19Q48BWm1aJQhIt6BF9z-uQ
code：j3p2
For more information about this dataset, please see Dataset Description. If the link fails, please feel free to contact us.
In the following content, we will take the partially occluded dataset on STL-10 (i.e., NLOS_Passive/STL-10/stl10_dark_1_d100_occluder.zip) as an example to illustrate the training and testing process. The dataset also contains test data from other data (MNIST, supermodel faces and real scenes), which can be used to evaluate the generalization ability of NLOS-OT.
Similarly, you can also use other datasets to complete training and testing according to the content below.
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Organize the files structure of the dataset
- Unzip the dataset
The size of a group projection dataset on STL-10 is about 30GB, which exceeds the maximum limit of Baiduyun. Therefore, we divided each group of data into two compressed packages (.z01 and .zip). You can decompress with
cd /pathtodataset/ zip -s 0 stl10_dark_1_d100_occluder.zip --out stl10_dark_1_d100_occluder_single.zip unzip stl10_dark_1_d100_occluder_single.zip # delete temp zip rm -rf ./stl10_dark_1_d100_occluder_single.zip # rename the folder mkdir ./C_dark_1_d100_occluder/ mv ./pro ./C_dark_1_d100_occluder/train
The GT folder can be obtained by
# Download gt zip. e.g., GT_stl10_allimages.zip # unzip and rename unzip GT_stl10_allimages.zip mkdir ./B/ mv ./GT_stl10_allimages ./B/train
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Organize the dataset (rename and move)
We recommend organizing the data files according to the following structure:
. ├── B │ └── train │ └── 1.png │ ... ├── C_dark_1_d100_wall │ └── train │ └── 1.png │ ... ├── C_dark_2_d100_wall │ └── train │ └── 1.png │ ... └── test ├── C_dark_1_d100_wall_test │ └── 2.png │ ... ├── C_dark_1_d100_wall_val │ └── 3.png │ ... ├── C_dark_2_d100_wall_test │ └── 2.png │ ... ├── C_dark_2_d100_wall_val │ └── 3.png │ ... ├── gt_test │ └── 2.png │ ... └── gt_val └── 3.png ...In our paper, we organize the data files using the following commands:
- For STL-10 (including real-world images)
# gt mkdir ./stl10_genera/chaomo_withstl10 mkdir ./stl10_genera/MNIST_withstl10 mkdir ./stl10_genera/real_withstl10 mkdir ./test/gt_test mkdir ./test/gt_val mv ./B/train/*11.png ./test/gt_val mv ./B/train/*31.png ./test/gt_val mv ./B/train/*51.png ./test/gt_val mv ./B/train/*71.png ./test/gt_val mv ./B/train/*21.png ./test/gt_test mv ./B/train/*41.png ./test/gt_test mv ./B/train/*61.png ./test/gt_test mv ./B/train/*81.png ./test/gt_test mv ./B/train/testchaomo* ./stl10_genera/chaomo_withstl10 mv ./B/train/testmnist* ./stl10_genera/MNIST_withstl10 mv ./B/train/testreal* ./stl10_genera/real_withstl10 # projection images mkdir ./stl10_genera/C_dark_1_d100_occluder_test mkdir ./stl10_genera/C_dark_1_d100_occluder_test/real mkdir ./stl10_genera/C_dark_1_d100_occluder_test/MNIST mkdir ./stl10_genera/C_dark_1_d100_occluder_test/chaomo mkdir ./test/C_dark_1_d100_occluder_test mkdir ./test/C_dark_1_d100_occluder_val mv ./C_dark_1_d100_occluder/testreal* ./stl10_genera/C_dark_1_d100_occluder_test/real mv ./C_dark_1_d100_occluder/testmnist* ./stl10_genera/C_dark_1_d100_occluder_test/MNIST mv ./C_dark_1_d100_occluder/testchaomo* ./stl10_genera/C_dark_1_d100_occluder_test/chaomo mv ./C_dark_1_d100_occluder/*11.png ./test/C_dark_1_d100_occluder_val mv ./C_dark_1_d100_occluder/*31.png ./test/C_dark_1_d100_occluder_val mv ./C_dark_1_d100_occluder/*51.png ./test/C_dark_1_d100_occluder_val mv ./C_dark_1_d100_occluder/*71.png ./test/C_dark_1_d100_occluder_val mv ./C_dark_1_d100_occluder/*21.png ./test/C_dark_1_d100_occluder_test mv ./C_dark_1_d100_occluder/*41.png ./test/C_dark_1_d100_occluder_test mv ./C_dark_1_d100_occluder/*61.png ./test/C_dark_1_d100_occluder_test mv ./C_dark_1_d100_occluder/*81.png ./test/C_dark_1_d100_occluder_test
- For supermodel faces / MNIST / anime faces
# gt mkdir ./test/gt_test mkdir ./test/gt_val mv ./B/train/5* ./test/gt_val mv ./test/C_dark_1_d100_wall_val/51* ./test/gt_test mv ./test/C_dark_1_d100_wall_val/55* ./test/gt_test mv ./test/C_dark_1_d100_wall_val/59* ./test/gt_test # projection images mkdir ./test/C_dark_1_d100_wall_test mkdir ./test/C_dark_1_d100_wall_val mv ./C_dark_1_d100_wall/train/5* ./test/C_dark_1_d100_wall_val mv ./test/C_dark_1_d100_wall_val/51* ./test/C_dark_1_d100_wall_test mv ./test/C_dark_1_d100_wall_val/55* ./test/C_dark_1_d100_wall_test mv ./test/C_dark_1_d100_wall_val/59* ./test/C_dark_1_d100_wall_test
The above structure can be applied to the training command we provided. You can also customize your own file structure and modify the corresponding parameters (--datarootTarget, --datarootData, --datarootValTarget, --datarootValData) in the command.
Before that, you should have installed the required packages and organized the data set according to the appropriate file structure.
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Download pretrained pth
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run the test.py
Before that, you should have installed the required packages and organized the data set according to the appropriate file structure.
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Feel free to use the dataset / code, but please cite:
- Ruixu Geng, Yang Hu, Zhi Lu, Cong Yu, Houqiang Li, Hengyu Zhang and Yan Chen, “Passive Non-Line-of-Sight Imaging Using Optimal Transport,” IEEE Transactions on Image Processing, 2021
@article{nlosot, author={Geng, Ruixu and Hu, Yang and Lu, Zhi and Yu, Cong and Li, Houqiang and Zhang, Hengyu and Chen, Yan}, journal={IEEE Transactions on Image Processing}, title={Passive Non-Line-of-Sight Imaging Using Optimal Transport}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TIP.2021.3128312}} -
You may also be interested in our review article:
- Ruixu Geng, Yang Hu, and Yan Chen, “Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes,” APSIPA Transactions on Signal and Information Processing, 2021
If you think it is helpful, please consider citing
@article{RecentAdvancesNLOS, author = {Geng, Ruixu and Hu, Yang and Chen, Yan}, title = {Recent Advances on Non-Line-of-Sight Imaging: Conventional Physical Models, Deep Learning, and New Scenes}, journal = {APSIPA Transactions on Signal and Information Processing}, year = {2021} } -
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