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

Andre M. Schreiber, Minsik Hong, Jerzy W. Rozenblit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2121-2126
Number of pages6
ISBN (Electronic)9781665442077
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia
Duration: Oct 17 2021Oct 20 2021

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/TerritoryAustralia
CityMelbourne
Period10/17/2110/20/21

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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