{"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/towards-deep-learning-based-6d-bin-pose","title":"Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans","arxiv_id":"2112.09598","date":"2021-12-17","proceeding":null,"authors":["Lukáš Gajdošech","Viktor Kocur","Martin Stuchlík","Lukáš Hudec","Martin Madaras"],"abstract":"An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.","url_abs":"https://arxiv.org/abs/2112.09598v1","url_pdf":"https://arxiv.org/pdf/2112.09598v1.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":"towards-deep-learning-based-6d-bin-pose","repo_url":"https://github.com/gajdosech2/bin-detect","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"6d-pose-estimation-1","task_name":"6D Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"3d-bsls-6d","name":"3D-BSLS-6D","full_name":"3D scans of Bins by Structured-Light Scanner for 6D pose estimation"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/6d-pose-estimation-on-3d-bsls-6d","task":"6D Pose Estimation","dataset":"3D-BSLS-6D","model":"VISAPP Baseline","rank_in_archive_order":1,"of":1,"metrics":{"eRE":"0.197","eTE":"3.469"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}