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dc.contributor.authorSchreiber, Andre M.
dc.contributor.authorHong, Minsik
dc.contributor.authorRozenblit, Jerzy W.
dc.date.accessioned2022-03-18T22:42:28Z
dc.date.available2022-03-18T22:42:28Z
dc.date.issued2021-10-17
dc.identifier.citationSchreiber, A. M., Hong, M., & Rozenblit, J. W. (2021). Monocular Depth Estimation using Synthetic Data for an Augmented Reality Training System in Laparoscopic Surgery. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics.en_US
dc.identifier.issn1062-922X
dc.identifier.doi10.1109/smc52423.2021.9658708
dc.identifier.urihttp://hdl.handle.net/10150/663680
dc.description.abstractDepth estimation is an important challenge in the field of augmented reality. Supervised deep learning methods of depth estimation can be difficult to use in novel settings due to the need for labeled training data. The work presented in this paper overcomes the challenge in a laparoscopic surgical simulation environment by using synthetic data generation for RGB-D training data. We also provide a neural network architecture that can generate real-time 448x448 depth map outputs suitable for use in AR applications. Our approach shows satisfactory performance when tested on a non-synthetic test dataset with an RMSE of 2.50 cm, MAE of 1.04 cm, and δ < 1.25 of 0.987.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2021 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.source2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
dc.titleMonocular Depth Estimation using Synthetic Data for an Augmented Reality Training System in Laparoscopic Surgeryen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Arizona, Department of Electrical and Computer Engineeringen_US
dc.identifier.journalConference Proceedings - IEEE International Conference on Systems, Man and Cyberneticsen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
refterms.dateFOA2022-03-18T22:42:29Z


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