Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 1
293-297

Monocular depth estimation based on light-weight pyramid decoder convolution neural network

Jia Ruiming
Li Tong
Li Yang
Wang Yiding
School of Information Science & Technology, North China University of Technology, Beijing 100144, China
DOI: 10.19734/j.issn.1001-3695.2019.09.0580

Abstract

This paper proposed a light-weight pyramid decoder convolution neural network(LPDNet) for monocular depth estimation, which could reduce the complexity and the computation time of the network model while ensuring the estimation accuracy. LPDNet was based on encoder-decoder structure to estimate the depth map of a monocular image in an end-to-end manner. The encoder network adopted ResNet50. The main part of decoder network was light-weight pyramid decoder(LPD) module, which learned representations from a large receptive field with fewer parameters by using depth-wise dilated separable convolutions and group convolutions. LPD module fused feature maps of different receptive fields through pyramid structure. Besides, in order to perform better knowledge sharing for estimation accuracy, it added deconvolution skip connection between adjacent decoder modules. Experiments on NYUD v2 dataset demonstrate that compared with the structured attention guided network in CVPR2018, the error of LPDNet is reduced by about 11.0% in RMS, and computational efficiency is about 84.6% higher.

Foundation Support

国家自然科学基金面上项目(61673021)
北方工业大学学生科技活动资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.09.0580
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 1
Section: Technology of Graphic & Image
Pages: 293-297
Serial Number: 1001-3695(2021)01-059-0293-05

Publish History

[2021-01-05] Printed Article

Cite This Article

贾瑞明, 李彤, 李阳, 等. 轻量金字塔解码结构的单目深度估计网络 [J]. 计算机应用研究, 2021, 38 (1): 293-297. (Jia Ruiming, Li Tong, Li Yang, et al. Monocular depth estimation based on light-weight pyramid decoder convolution neural network [J]. Application Research of Computers, 2021, 38 (1): 293-297. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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