Official codes of CVPR 2023 Paper | Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
Create a new environment and install dependencies with requirement.txt:
conda create -n NLOS_Tracking
conda activate NLOS_Tracking
conda install --file requirements.txtThe NLOS-Track dataset can be downloaded from kaggle.
The file structure in project root should be as follow:
project_root
| README.md
| requirements.txt
| train.py
+---data
+---utils
+---configs
| ...
+---dataset
+---render
| +---0000
| | configs.yaml
| | route.mat
| | video_128.npy
| | 001.png
| | 002.png
| | ...
| +---0001
| ...
+---real-shot
+---0000
| route.mat
| video_128.npy
+---0001
...
Follow the code blocks in data_playground.ipynb to load and visualize the dataset.
Before training, fill the missing items in configuration files.
Create a new configuration file in ./configs for training:
python train.py --cfg_file=new_cfg --model_name=PAC_Netor directly use default.yaml by default:
python train.py --model_name=PAC_Net --pretrained -b 64 -lr_b 2.5e-4 --gpu_ids=0,1 --port=8888Follow the code blocks in test.ipynb to test a trained model.
@article{wang2023nlosTrack,
author = {Wang, Yihao and Wang, Zhigang and Zhao, Bin and Wang, Dong and Chen, Mulin and Li, Xuelong},
title = {Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking},
journal = {CVPR},
year = {2023},
}
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