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dc.contributor.authorWu, Jimmy
dc.contributor.authorZhou, Bolei
dc.contributor.authorRussell, Rebecca
dc.contributor.authorKee, Vincent
dc.contributor.authorWagner, Syler
dc.contributor.authorHebert, Mitchell
dc.contributor.authorTorralba, Antonio
dc.contributor.authorJohnson, David M.S.
dc.date.accessioned2020-01-20T18:35:05Z
dc.date.available2020-01-20T18:35:05Z
dc.date.issued2019-01-07
dc.date.submitted2018-08-03
dc.identifier.isbn9781538680940
dc.identifier.isbn9781538680933
dc.identifier.isbn9781538680957
dc.identifier.issn2153-0866
dc.identifier.issn2153-0858
dc.identifier.urihttps://hdl.handle.net/1721.1/123478
dc.description.abstractIn this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an intermediate representation to bridge real and synthetic. We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data. Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement. Keywords: pose estimation; image segmentation; three-dimensional displays; quaternions; real-time systems; training; task analysisen_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/iros.2018.8593662en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleReal-Time Object Pose Estimation with Pose Interpreter Networksen_US
dc.typeArticleen_US
dc.identifier.citationWu, Jimmy et al. "Real-Time Object Pose Estimation with Pose Interpreter Networks." 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 1-5, 2018, Madrid, Spain, IEEE, 2019en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journal2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-07-11T17:18:12Z
dspace.date.submission2019-07-11T17:18:13Z


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