{"id":16107172,"url":"https://github.com/againstentropy/nlos-track","last_synced_at":"2025-03-18T08:32:17.967Z","repository":{"id":174089790,"uuid":"615991204","full_name":"AgainstEntropy/NLOS-Track","owner":"AgainstEntropy","description":"Official codes of CVPR 2023 Paper | Propagate And Calibrate: Real-time Passive Non-line-of-sight 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NLOS Tracking\n\n[![Project Website](https://img.shields.io/badge/Project-Website-orange)](https://againstentropy.github.io/NLOS-Track/)\n[![arXiv](https://img.shields.io/badge/arXiv-2303.11791-b31b1b.svg)](https://arxiv.org/abs/2303.11791)\n[![Dataset Download](https://img.shields.io/badge/Dataset-Download-blue)](https://www.kaggle.com/datasets/againstentropy1/nlos-track)\n\n\nOfficial codes of CVPR 2023 [Paper](https://arxiv.org/abs/2303.11791) | _Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking_\n\n## Prepreation\n\n### Environment\n\nCreate a new environment and install dependencies with `requirement.txt`:\n\n```shell\nconda create -n NLOS_Tracking\n\nconda activate NLOS_Tracking\n\nconda install --file requirements.txt\n```\n\n### Data \n\nThe NLOS-Track dataset can be downloaded from [kaggle](https://www.kaggle.com/datasets/againstentropy1/nlos-track).\n\nThe file structure in project root should be as follow:\n\n```\nproject_root\n|   README.md\n|   requirements.txt\n|   train.py\n+---data\n+---utils\n+---configs\n|   ...\n+---dataset\n    +---render\n    |   +---0000\n    |   |      configs.yaml\n    |   |      route.mat\n    |   |      video_128.npy\n    |   |      001.png\n    |   |      002.png\n    |   |      ...\n    |   +---0001\n    |       ...\n    +---real-shot\n        +---0000\n        |      route.mat\n        |      video_128.npy\n        +---0001\n            ...\n```\n\n#### Data Loading and Visualization\n\nFollow the code blocks in `data_playground.ipynb` to load and visualize the dataset.\n\n## Usage\n\n### Train\n\n**Before training, fill the missing items in configuration files.**\n\nCreate a new configuration file in `./configs` for training:\n\n```shell\npython train.py --cfg_file=new_cfg --model_name=PAC_Net\n```\n\nor directly use `default.yaml` by default:\n\n```shell\npython train.py --model_name=PAC_Net --pretrained -b 64 -lr_b 2.5e-4 --gpu_ids=0,1 --port=8888\n```\n\n### Test\n\nFollow the code blocks in `test.ipynb` to test a trained model.\n\n## Citation\n\n```bibtex\n@article{wang2023nlosTrack,\n  author   = {Wang, Yihao and Wang, Zhigang and Zhao, Bin and Wang, Dong and Chen, Mulin and Li, Xuelong},\n  title    = {Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking},\n  journal  = {CVPR},\n  year     = {2023},\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagainstentropy%2Fnlos-track","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fagainstentropy%2Fnlos-track","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagainstentropy%2Fnlos-track/lists"}