An efficient encoder–decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data

Depth estimation from a single image frame is a fundamental challenge in computer vision, with many applications such as augmented reality, action recognition, image understanding, and autonomous driving. Large and diverse training sets are required for accurate depth estimation from a single image...

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Veröffentlicht in:	Neural networks Jg. 142; S. 479 - 491
Hauptverfasser:	Khan, Faisal, Hussain, Shahid, Basak, Shubhajit, Lemley, Joseph, Corcoran, Peter
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.10.2021
Schlagworte:	2.5D dataset Convolution neural network Depth estimation Encoder–decoder architecture Facial depth Hybrid loss function Hybrid loss function Encoder–decoder architecture 2.5D dataset Depth estimation Convolution neural network Facial depth
ISSN:	0893-6080, 1879-2782, 1879-2782
Online-Zugang:	Volltext
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