{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:23:46Z","timestamp":1762608226368,"version":"3.41.2"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Virtual Real."],"abstract":"<jats:p>The solutions to many computer vision problems, including that of 6D object pose estimation, are dominated nowadays by the explosion of the learning-based paradigm. In this paper, we investigate 6D object pose estimation in a practical, real-word setting in which a mobile device (smartphone\/tablet) needs to be localized in front of a museum exhibit, in support of an augmented-reality application scenario. In view of the constraints and the priorities set by this particular setting, we consider an appropriately tailored classical as well as a learning-based method. Moreover, we develop a hybrid method that consists of both classical and learning based components. All three methods are evaluated quantitatively on a standard, benchmark dataset, but also on a new dataset that is specific to the museum guidance scenario of interest.<\/jats:p>","DOI":"10.3389\/frvir.2021.649784","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T06:13:54Z","timestamp":1616652834000},"update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Toward Augmented Reality in Museums: Evaluation of Design Choices for 3D Object Pose Estimation"],"prefix":"10.3389","volume":"2","author":[{"given":"Paschalis","family":"Panteleris","sequence":"first","affiliation":[]},{"given":"Damien","family":"Michel","sequence":"additional","affiliation":[]},{"given":"Antonis","family":"Argyros","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"B1","first-page":"404","article-title":"\u201cSurf: speeded up robust features,\u201d","volume-title":"European Conference on Computer Vision","author":"Bay","year":"2006"},{"key":"B2","doi-asserted-by":"crossref","first-page":"3364","DOI":"10.1109\/CVPR.2016.366","article-title":"\u201cUncertainty-driven 6D pose estimation of objects and scenes from a single RGB image,\u201d","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Brachmann","year":"2016"},{"key":"B3","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/CVPR.2010.5540108","article-title":"\u201cModel globally, match locally: efficient and robust 3D object recognition,\u201d","volume-title":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"Drost","year":"2010"},{"key":"B4","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"\u201cDeep residual learning for image recognition,\u201d","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He","year":"2016"},{"key":"B5","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/978-3-642-37331-2_42","article-title":"\u201cModel based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes,\u201d","volume-title":"Computer Vision - ACCV 2012","author":"Hinterstoisser","year":"2013"},{"key":"B6","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/978-3-319-49409-8_52","article-title":"\u201cOn evaluation of 6D object pose estimation,\u201d","volume-title":"Computer Vision - ECCV 2016 Workshops. 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