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Proceedings Paper

Monocular depth estimation based on unsupervised learning
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Paper Abstract

Due to the low cost and easy deployment, the depth estimation of monocular cameras has always attracted attention of researchers. As good performance based on deep learning technology in depth estimation, more and more training models has emerged for depth estimation. Most existing works have required very promising results that belongs to supervised learning methods, but corresponding ground truth depth data for training is inevitable that makes training complicated. To overcome this limitation, an unsupervised learning framework is used for monocular depth estimation from videos, which contains depth map and pose network. In this paper, better results can be achieved by optimizing training models and improving training loss. Besides, training and evaluation data is based on standard dataset KITTI (Karlsruhe Institute of Technology and Toyota Institute of Technology). In the end, the results are shown through comparing with different training models used in this paper.

Paper Details

Date Published: 18 November 2019
PDF: 9 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 1118704 (18 November 2019); doi: 10.1117/12.2537957
Show Author Affiliations
Wan Liu, Zhejiang Univ. (China)
Yan Sun, Zhejiang Univ. (China)
XuCheng Wang, Zhejiang Univ. (China)
Lin Yang, Zhejiang Univ. (China)
Zhenrong Zheng, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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