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Depth Map Upsampling via Multi-Modal Generative Adversarial Network
Open AccessArticle

Single-Image Depth Inference Using Generative Adversarial Networks

1
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
2
Software Technology Department, De La Salle University, Manila 1004, Philippines
3
Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1708; https://doi.org/10.3390/s19071708
Received: 16 February 2019 / Revised: 1 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
(This article belongs to the Special Issue Selected Papers from INNOV 2018)
Depth has been a valuable piece of information for perception tasks such as robot grasping, obstacle avoidance, and navigation, which are essential tasks for developing smart homes and smart cities. However, not all applications have the luxury of using depth sensors or multiple cameras to obtain depth information. In this paper, we tackle the problem of estimating the per-pixel depths from a single image. Inspired by the recent works on generative neural network models, we formulate the task of depth estimation as a generative task where we synthesize an image of the depth map from a single Red, Green, and Blue (RGB) input image. We propose a novel generative adversarial network that has an encoder-decoder type generator with residual transposed convolution blocks trained with an adversarial loss. Quantitative and qualitative experimental results demonstrate the effectiveness of our approach over several depth estimation works. View Full-Text
Keywords: depth estimation; encoder-decoder networks; generative adversarial networks depth estimation; encoder-decoder networks; generative adversarial networks
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MDPI and ACS Style

Tan, D.S.; Yao, C.-Y.; Ruiz, C., Jr.; Hua, K.-L. Single-Image Depth Inference Using Generative Adversarial Networks. Sensors 2019, 19, 1708.

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