MATEC Web Conf.
Volume 175, 20182018 International Forum on Construction, Aviation and Environmental Engineering-Internet of Things (IFCAE-IOT 2018)
|Number of page(s)||5|
|Section||Computer Simulation and Design|
|Published online||02 July 2018|
Depth Estimation from Monocular Image and Coarse Depth Points based on Conditional GAN
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2 Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
Corresponding author : firstname.lastname@example.org
Depth estimation has achieved considerable success with the development of the depth sensor devices and deep learning method. However, depth estimation from monocular RGB-based image will increase ambiguity and is prone to error. In this paper, we present a novel approach to produce dense depth map from a single image coupled with coarse point-cloud samples. Our approach learns to fit the distribution of the depth map from source data using conditional adversarial networks and convert the sparse point clouds to dense maps. Our experiments show that the use of the conditional adversarial networks can add full image information to the predicted depth maps and the effectiveness of our approach to predict depth in NYU-Depth-v2 indoor dataset.
© The Authors, published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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