{"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/densefusion-6d-object-pose-estimation-by","title":"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion","arxiv_id":"1901.04780","date":"2019-01-15","proceeding":"CVPR 2019 6","authors":["Chen Wang","Danfei Xu","Yuke Zhu","Roberto Martín-Martín","Cewu Lu","Li Fei-Fei","Silvio Savarese"],"abstract":"A key technical challenge in performing 6D object pose estimation from RGB-D\nimage is to fully leverage the two complementary data sources. Prior works\neither extract information from the RGB image and depth separately or use\ncostly post-processing steps, limiting their performances in highly cluttered\nscenes and real-time applications. In this work, we present DenseFusion, a\ngeneric framework for estimating 6D pose of a set of known objects from RGB-D\nimages. DenseFusion is a heterogeneous architecture that processes the two data\nsources individually and uses a novel dense fusion network to extract\npixel-wise dense feature embedding, from which the pose is estimated.\nFurthermore, we integrate an end-to-end iterative pose refinement procedure\nthat further improves the pose estimation while achieving near real-time\ninference. Our experiments show that our method outperforms state-of-the-art\napproaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed\nmethod to a real robot to grasp and manipulate objects based on the estimated\npose.","url_abs":"http://arxiv.org/abs/1901.04780v1","url_pdf":"http://arxiv.org/pdf/1901.04780v1.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":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/RiplleYang/DenseFusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/Theopetitjean/DenseFusion_R_Invariant","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/Yotonctu/densefusion_torch1.0","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/caoquan95/6D-pose-project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/cxt98/Densefusion-transparency","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/hz-ants/DenseFusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/j96w/DenseFusion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"densefusion-6d-object-pose-estimation-by","repo_url":"https://github.com/shayeree96/Adversial-Attacks-on-densely-fused-point-clouds-for-6D-Pose-Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"6d-pose-estimation-1","task_name":"6D Pose Estimation"},{"task_slug":"6d-pose-estimation-using-rgbd","task_name":"6D Pose Estimation using RGBD"},{"task_slug":"object","task_name":"Object"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-detection-on-dttd2","task":"3D Object Detection","dataset":"DTTD-Mobile","model":"DenseFusion","rank_in_archive_order":2,"of":5,"metrics":{"ADD AUC":"69.67","ADD-S AUC":"85.88"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-on-linemod-2","task":"6D Pose Estimation","dataset":"LineMOD","model":"DenseFusion","rank_in_archive_order":4,"of":5,"metrics":{"Accuracy (ADD)":"94.3"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-on-ycb-video-2","task":"6D Pose Estimation","dataset":"YCB-Video","model":"DenseFusion","rank_in_archive_order":9,"of":10,"metrics":{"ADDS AUC":"93.1"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-using-rgbd-on-linemod","task":"6D Pose Estimation using RGBD","dataset":"LineMOD","model":"DeepFusion","rank_in_archive_order":5,"of":8,"metrics":{"Mean ADD":"94.3"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.04780","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.04780"}},"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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