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    Monocular Depth Estimation using Synthetic Data for an Augmented Reality Training System in Laparoscopic Surgery

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    SMC21_0521_Final.pdf
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    Description:
    Final Accepted Manuscript
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    Author
    Schreiber, Andre M.
    Hong, Minsik
    Rozenblit, Jerzy W.
    Affiliation
    University of Arizona, Department of Electrical and Computer Engineering
    Issue Date
    2021-10-17
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    Schreiber, 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.
    Journal
    Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
    Rights
    © 2021 IEEE.
    Collection Information
    This 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.
    Abstract
    Depth 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.
    Note
    Immediate access
    ISSN
    1062-922X
    DOI
    10.1109/smc52423.2021.9658708
    Version
    Final accepted manuscript
    Sponsors
    National Science Foundation
    ae974a485f413a2113503eed53cd6c53
    10.1109/smc52423.2021.9658708
    Scopus Count
    Collections
    UA Faculty Publications

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