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Sensors 2018, 18(5), 1318; https://doi.org/10.3390/s18051318

Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model

1,*
,
2,*
and
2
1
Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
2
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Received: 30 March 2018 / Revised: 19 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Depth Sensors and 3D Vision)
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Abstract

This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results. View Full-Text
Keywords: depth reconstruction; single image; convolutional neural network; conditional random field depth reconstruction; single image; convolutional neural network; conditional random field
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Liu, D.; Liu, X.; Wu, Y. Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model. Sensors 2018, 18, 1318.

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