Relativistic Approach for Training Self-Supervised Adversarial Depth Prediction Model Using Symmetric Consistency

Relativistic Approach for Training Self-Supervised Adversarial Depth Prediction Model Using Symmetric Consistency

This article proposes a novel approach for predicting depth from a single image in a self-supervised manner, specifically using generative adversarial networks (GANs) with enhancements to improve training performance. We train our model to generate disparity maps or inverse depth maps using left/rig...

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Bibliographic Details
Main Authors: Hyeseung Park, Seungchul Park, Youngbok Joo
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Self-supervised depth prediction
monocular depth estimation
relativistic GAN
GMSD
perceptual loss
Online Access:https://ieeexplore.ieee.org/document/9252913/

Internet

https://ieeexplore.ieee.org/document/9252913/

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