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. 2019 Mar 6;9:3744. doi: 10.1038/s41598-019-40064-0

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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(a) Depth map from the developed setup (raw data, no post-processing). (b) Conventional Image of scene for display purposes. (c) Depth map from the Kinect used as reference. The yellow square corresponds to the field of view. (d) Relative error for this scene, with the output of a Microsoft Kinect as ground truth. A sparse set of handpicked points were selected in the ground truth and then compared to depth estimations from our network.