Using deep learning for depth estimation and 3D reconstruction of humans

Alexander Freller, Dora Turk, Gerald A. Zwettler

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

Abstract

Deep learning for depth estimation from monocular video feed is a common strategy to get rough 3D surface information when an RGB-D camera is not present. Depth information is of importance in many domains such as object localization, tracking, and scene reconstruction in robotics and industrial environments from multiple camera views. The convolutional neural networks UpProjection, DORN, and Encoder/Decoder are evaluated on hybrid training datasets enriched by CGI data. The highest accuracy results are derived from the UpProjection network with a relative deviation of 1.77% to 2.69% for CAD-120 and SMV dataset respectively. It is shown, that incorporation of front and side view allows to increase the achievable depth estimation for human body images. With the incorporation of a second view the error is reduced from 6.69% to 6.16%. For the target domain of this depth estimation, the 3D human body reconstruction from aligned images in T-pose, plain silhouette reconstruction generally leads to acceptable results. Nevertheless, additionally incorporating the rough depth approximation in the future, concave areas at the chest, breast, and buttocks, currently not handled by the silhouette reconstruction, can result in more realistic 3D body models by utilizing the deep learning outcome in a hybrid approach.

Original languageEnglish
Title of host publication32nd European Modeling and Simulation Symposium, EMSS 2020
EditorsMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Antonella Petrillo
PublisherDIME UNIVERSITY OF GENOA
Pages281-287
Number of pages7
ISBN (Electronic)9788885741454
DOIs
Publication statusPublished - 2020
Event32nd European Modeling and Simulation Symposium, EMSS 2020 - Virtual, Online
Duration: 16 Sep 202018 Sep 2020

Publication series

Name32nd European Modeling and Simulation Symposium, EMSS 2020

Conference

Conference32nd European Modeling and Simulation Symposium, EMSS 2020
CityVirtual, Online
Period16.09.202018.09.2020

Keywords

  • Convolutional Neural Networks
  • Deep Learning
  • Depth Estimation
  • Human Body 3D Reconstruction

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