{"id":16107173,"url":"https://github.com/againstentropy/netf","last_synced_at":"2025-04-06T03:40:23.119Z","repository":{"id":134209327,"uuid":"468215921","full_name":"AgainstEntropy/NeTF","owner":"AgainstEntropy","description":null,"archived":false,"fork":false,"pushed_at":"2022-03-10T06:26:08.000Z","size":60,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-12T09:57:45.995Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AgainstEntropy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-03-10T06:08:23.000Z","updated_at":"2022-03-10T06:17:45.000Z","dependencies_parsed_at":null,"dependency_job_id":"9b367b6d-873c-4637-9809-26a41cf7f0f1","html_url":"https://github.com/AgainstEntropy/NeTF","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AgainstEntropy%2FNeTF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AgainstEntropy%2FNeTF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AgainstEntropy%2FNeTF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AgainstEntropy%2FNeTF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AgainstEntropy","download_url":"https://codeload.github.com/AgainstEntropy/NeTF/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247430838,"owners_count":20937873,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-09T19:15:25.554Z","updated_at":"2025-04-06T03:40:23.098Z","avatar_url":"https://github.com/AgainstEntropy.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NeTF_public\nThe 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.)\n\nThe 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\n\nThe 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/)\n\nWe 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. \n\n# Environment setup\nMake sure that the dependcies in `requirements.txt` are installed, or they can be installed by \n```\n\"pip install -r requirements.txt\"\n```\n\n# How to run\nMake sure that data is place correctly like\n```\nNeTF_public\n│   README.md\n│   run_netf.py\n│   ...\n│\n└───data\n    │   fk_dragon_meas_180_min_256_preprocessed.mat\n    │   ...\n    │\n    └───zaragozadataset\n        │   zaragoza256_preprocessed.mat\n        │   ...\n \n```\nThen run with preset settings:\n```\n\"python run_netf.py --config configs/zaragoza_bunny.txt\"\n```\nDifferent settings are stroaged at \"./configs/\".\n\nUnder preset settings, the training process takes around 24 hours on a single NVIDIA Tesla M40 GPU.\n\n# Results\nThe final volume and slices from different view are stroaged at \"./model\"\n\nThe matlab script \"show_result.m\" is also provided to generate 2D images from different views and 3D density distribution.\n\nAnd the comparision between predicted and measured histogram is stroaged at \"./figure\"\n\n# Contact us\nPlease email shensy@shanghaitech.edu.cn or wangzi@shanghaitech.edu.cn if you have any questions or suggestions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagainstentropy%2Fnetf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fagainstentropy%2Fnetf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagainstentropy%2Fnetf/lists"}