{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ssd-6d-making-rgb-based-3d-detection-and-6d","title":"SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again","arxiv_id":"1711.10006","date":"2017-11-27","proceeding":"ICCV 2017 10","authors":["Wadim Kehl","Fabian Manhardt","Federico Tombari","Slobodan Ilic","Nassir Navab"],"abstract":"We present a novel method for detecting 3D model instances and estimating\ntheir 6D poses from RGB data in a single shot. To this end, we extend the\npopular SSD paradigm to cover the full 6D pose space and train on synthetic\nmodel data only. Our approach competes or surpasses current state-of-the-art\nmethods that leverage RGB-D data on multiple challenging datasets. Furthermore,\nour method produces these results at around 10Hz, which is many times faster\nthan the related methods. For the sake of reproducibility, we make our trained\nnetworks and detection code publicly available.","url_abs":"http://arxiv.org/abs/1711.10006v1","url_pdf":"http://arxiv.org/pdf/1711.10006v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ssd-6d-making-rgb-based-3d-detection-and-6d","repo_url":"https://github.com/wadimkehl/ssd-6d","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"6d-pose-estimation-1","task_name":"6D Pose Estimation"},{"task_slug":"6d-pose-estimation","task_name":"6D Pose Estimation using RGB"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"ssd","method_name":"SSD"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/6d-pose-estimation-on-linemod","task":"6D Pose Estimation using RGB","dataset":"LineMOD","model":"SSD-6D","rank_in_archive_order":16,"of":22,"metrics":{"Mean ADD":"76.3","Mean IoU":"99.4"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-on-occlusion","task":"6D Pose Estimation using RGB","dataset":"OCCLUSION","model":"SSD-6D","rank_in_archive_order":2,"of":2,"metrics":{"MAP":"0.38"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-using-rgbd-on-linemod","task":"6D Pose Estimation using RGBD","dataset":"LineMOD","model":"SSD-6D","rank_in_archive_order":6,"of":8,"metrics":{"Mean ADD":"90.9","Mean IoU":"96.5"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-using-rgbd-on-tejani","task":"6D Pose Estimation using RGBD","dataset":"Tejani","model":"SSD-6D","rank_in_archive_order":1,"of":1,"metrics":{"IoU-2D":"0.988","IoU-3D":"0.963","VSS-2D":"0.724","VSS-3D":"0.854"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.10006","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}