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Predicting Depth from Single RGB Images with Pyramidal Three-Streamed Networks

1
School of Technology, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China
2
Key Laboratory of State Forestry Administration on Forestry Equipment and Automation, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 667; https://doi.org/10.3390/s19030667
Received: 2 January 2019 / Revised: 1 February 2019 / Accepted: 2 February 2019 / Published: 6 February 2019
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Abstract

Predicting depth from a monocular image is an ill-posed and inherently ambiguous issue in computer vision. In this paper, we propose a pyramidal third-streamed network (PTSN) that recovers the depth information using a single given RGB image. PTSN uses pyramidal structure images, which can extract multiresolution features to improve the robustness of the network as the network input. The full connection layer is changed into fully convolutional layers with a new upconvolution structure, which reduces the network parameters and computational complexity. We propose a new loss function including scale-invariant, horizontal and vertical gradient loss that not only helps predict the depth values, but also clearly obtains local contours. We evaluate PTSN on the NYU Depth v2 dataset and the experimental results show that our depth predictions have better accuracy than competing methods. View Full-Text
Keywords: predicting depth; monocular image; third-streamed network; pyramidal predicting depth; monocular image; third-streamed network; pyramidal
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Chen, S.; Tang, M.; Kan, J. Predicting Depth from Single RGB Images with Pyramidal Three-Streamed Networks. Sensors 2019, 19, 667.

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