{"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/bop-benchmark-for-6d-object-pose-estimation","title":"BOP: Benchmark for 6D Object Pose Estimation","arxiv_id":"1808.08319","date":"2018-08-24","proceeding":"ECCV 2018 9","authors":["Tomas Hodan","Frank Michel","Eric Brachmann","Wadim Kehl","Anders Glent Buch","Dirk Kraft","Bertram Drost","Joel Vidal","Stephan Ihrke","Xenophon Zabulis","Caner Sahin","Fabian Manhardt","Federico Tombari","Tae-Kyun Kim","Jiri Matas","Carsten Rother"],"abstract":"We propose a benchmark for 6D pose estimation of a rigid object from a single\nRGB-D input image. The training data consists of a texture-mapped 3D object\nmodel or images of the object in known 6D poses. The benchmark comprises of: i)\neight datasets in a unified format that cover different practical scenarios,\nincluding two new datasets focusing on varying lighting conditions, ii) an\nevaluation methodology with a pose-error function that deals with pose\nambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that\ncaptures the status quo of the field, and iv) an online evaluation system that\nis open for continuous submission of new results. The evaluation shows that\nmethods based on point-pair features currently perform best, outperforming\ntemplate matching methods, learning-based methods and methods based on 3D local\nfeatures. The project website is available at bop.felk.cvut.cz.","url_abs":"http://arxiv.org/abs/1808.08319v1","url_pdf":"http://arxiv.org/pdf/1808.08319v1.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":"bop-benchmark-for-6d-object-pose-estimation","repo_url":"https://github.com/thodan/bop_toolkit","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"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":"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"},{"task_slug":"template-matching","task_name":"Template Matching"}],"methods":[],"datasets_introduced":[{"slug":"tud-l","name":"TUD-L","full_name":""},{"slug":"tyo-l","name":"TYO-L","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.08319","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}