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dc.contributor.authorJalal, Monaen_US
dc.contributor.authorSpjut, Josefen_US
dc.contributor.authorBoudaoud, Benen_US
dc.contributor.authorBetke, Margriten_US
dc.coverage.spatialLong Beach, CAen_US
dc.date.accessioned2020-05-18T13:58:16Z
dc.date.available2020-05-18T13:58:16Z
dc.date.issued2019-06-16
dc.identifierhttp://openaccess.thecvf.com/content_CVPRW_2019/html/WiCV/Jalal_SIDOD_A_Synthetic_Image_Dataset_for_3D_Object_Pose_Recognition_CVPRW_2019_paper.html
dc.identifier.citationMona Jalal, Josef Spjut, Ben Boudaoud, M. Betke. 2019. "SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors." IEEE Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, CA, 2019-06-16 - 2019-06-20. https://doi.org/10.1109/CVPRW.2019.00063
dc.identifier.urihttps://hdl.handle.net/2144/40959
dc.description.abstractWe present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset ) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.en_US
dc.rightsCopyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.en_US
dc.subjectThree-dimensional displaysen_US
dc.subjectPose estimationen_US
dc.subjectCamerasen_US
dc.subjectImage segmentationen_US
dc.subjectComputer visionen_US
dc.subjectTrainingen_US
dc.subjectLightingen_US
dc.subjectNVIDIA Deep Learning Data Synthesizeren_US
dc.subjectSIDODen_US
dc.subjectVirtual realityen_US
dc.subjectYCB dataseten_US
dc.titleSIDOD: a synthetic image dataset for 3D object pose recognition with distractorsen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1109/CVPRW.2019.00063
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-4491-6868 (Betke, M)
dc.identifier.mycv547475


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