{"id":20062259,"url":"https://github.com/cuge1995/u6da","last_synced_at":"2025-03-02T10:23:14.189Z","repository":{"id":117639100,"uuid":"464733608","full_name":"cuge1995/U6DA","owner":"cuge1995","description":"official Pytorch implementation of paper 'Adversarial samples for deep monocular 6D object pose estimation'","archived":false,"fork":false,"pushed_at":"2022-05-29T01:48:55.000Z","size":5,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-12T22:33:00.498Z","etag":null,"topics":["6d","6dof-pose","adversarial-attacks","adversarial-defense","adversarial-examples","adversarial-machine-learning","pose-estimation"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2203.00302","language":null,"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/cuge1995.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":"2022-03-01T03:43:17.000Z","updated_at":"2024-10-26T09:38:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"05527d0e-8dfe-4d42-b8ba-a6db1395b21b","html_url":"https://github.com/cuge1995/U6DA","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/cuge1995%2FU6DA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FU6DA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FU6DA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cuge1995%2FU6DA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cuge1995","download_url":"https://codeload.github.com/cuge1995/U6DA/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241489542,"owners_count":19971072,"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":["6d","6dof-pose","adversarial-attacks","adversarial-defense","adversarial-examples","adversarial-machine-learning","pose-estimation"],"created_at":"2024-11-13T13:28:10.972Z","updated_at":"2025-03-02T10:23:14.160Z","avatar_url":"https://github.com/cuge1995.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# U6DA\nofficial Pytorch implementation of paper '[Adversarial samples for deep monocular 6D object pose estimation](https://arxiv.org/abs/2203.00302)'\n\n\n## U6DA-Linemod\nThe dataset can be download from [Google Drive](https://drive.google.com/file/d/1jmELumR2CuIXv1urLykoHoFtiMdg9xWx/view?usp=sharing) and [Baidu Pan](https://pan.baidu.com/s/12VREdD1BqFRTUtE-laomJg) (code: jcfm)\n\nAfter download and unzip, back up the original data first, then:\n```\ncp ape/* lm/test/000001/rgb/\ncp benchvise/* lm/test/000002/rgb/\ncp cam/* lm/test/000004/rgb/\ncp can/* lm/test/000005/rgb/\ncp cat/* lm/test/000006/rgb/\ncp driller/* lm/test/000008/rgb/\ncp duck/* lm/test/000009/rgb/\ncp eggbox/* lm/test/000010/rgb/\ncp glue/* lm/test/000011/rgb/\ncp holepuncher/* lm/test/000012/rgb/\ncp iron/* lm/test/000013/rgb/\ncp lamp/* lm/test/000014/rgb/\ncp phone/* lm/test/000015/rgb/\n```\n\n* Our codes coming soon!\n\n## Citation\nif you find our work useful in your research, please consider citing:\n```\n@article{zhang2022adversarial,\n  title={Adversarial samples for deep monocular 6D object pose estimation},\n  author={Zhang, Jinlai and Li, Weiming and Liang, Shuang and Wang, Hao and Zhu, Jihong},\n  journal={arXiv preprint arXiv:2203.00302},\n  year={2022}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Fu6da","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcuge1995%2Fu6da","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuge1995%2Fu6da/lists"}