{"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/segmentation-driven-6d-object-pose-estimation","title":"Segmentation-driven 6D Object Pose Estimation","arxiv_id":"1812.02541","date":"2018-12-06","proceeding":"CVPR 2019 6","authors":["Yinlin Hu","Joachim Hugonot","Pascal Fua","Mathieu Salzmann"],"abstract":"The most recent trend in estimating the 6D pose of rigid objects has been to\ntrain deep networks to either directly regress the pose from the image or to\npredict the 2D locations of 3D keypoints, from which the pose can be obtained\nusing a PnP algorithm. In both cases, the object is treated as a global entity,\nand a single pose estimate is computed. As a consequence, the resulting\ntechniques can be vulnerable to large occlusions.\n  In this paper, we introduce a segmentation-driven 6D pose estimation\nframework where each visible part of the objects contributes a local pose\nprediction in the form of 2D keypoint locations. We then use a predicted\nmeasure of confidence to combine these pose candidates into a robust set of\n3D-to-2D correspondences, from which a reliable pose estimate can be obtained.\nWe outperform the state-of-the-art on the challenging Occluded-LINEMOD and\nYCB-Video datasets, which is evidence that our approach deals well with\nmultiple poorly-textured objects occluding each other. Furthermore, it relies\non a simple enough architecture to achieve real-time performance.","url_abs":"http://arxiv.org/abs/1812.02541v3","url_pdf":"http://arxiv.org/pdf/1812.02541v3.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":"segmentation-driven-6d-object-pose-estimation","repo_url":"https://github.com/cvlab-epfl/segmentation-driven-pose","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"segmentation-driven-6d-object-pose-estimation","repo_url":"https://github.com/AP-EPFL/DA-segmentation-driven-pose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"segmentation-driven-6d-object-pose-estimation","repo_url":"https://github.com/cvlab-epfl/single-stage-pose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"segmentation-driven-6d-object-pose-estimation","repo_url":"https://github.com/hz-ants/segmentation-driven-pose-train-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"segmentation-driven-6d-object-pose-estimation","repo_url":"https://github.com/sjtuytc/segmentation-driven-pose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"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":"object","task_name":"Object"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"pose-prediction","task_name":"Pose Prediction"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/6d-pose-estimation-using-rgb-on-occlusion","task":"6D Pose Estimation using RGB","dataset":"Occlusion LineMOD","model":"SegDriven","rank_in_archive_order":12,"of":13,"metrics":{"Mean ADD":"27"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-on-ycb-video","task":"6D Pose Estimation using RGB","dataset":"YCB-Video","model":"SegDriven","rank_in_archive_order":4,"of":5,"metrics":{"Accuracy (ADD)":"39.0%","Mean ADD":"39"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.02541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.02541"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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