{"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/rnnpose-recurrent-6-dof-object-pose","title":"RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization","arxiv_id":"2203.12870","date":"2022-03-24","proceeding":"CVPR 2022 1","authors":["Yan Xu","Kwan-Yee Lin","Guofeng Zhang","Xiaogang Wang","Hongsheng Li"],"abstract":"6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a non-linear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses. Furthermore, to improve the robustness to occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance.","url_abs":"https://arxiv.org/abs/2203.12870v3","url_pdf":"https://arxiv.org/pdf/2203.12870v3.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":"rnnpose-recurrent-6-dof-object-pose","repo_url":"https://github.com/decayale/rnnpose","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/6d-pose-estimation-on-linemod","task":"6D Pose Estimation using RGB","dataset":"LineMOD","model":"RNNPose","rank_in_archive_order":1,"of":22,"metrics":{"Accuracy (ADD)":"97.37%","Mean ADD":"97.37"},"uses_additional_data":false},{"leaderboard":"/sota/6d-pose-estimation-using-rgb-on-occlusion","task":"6D Pose Estimation using RGB","dataset":"Occlusion LineMOD","model":"RNNPose (Trained with synthetic data and LINEMOD training set, w/o pbr data)","rank_in_archive_order":2,"of":13,"metrics":{"Mean ADD":"60.65"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.12870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12870"}},"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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