Monocular depth estimation based on unsupervised learning

Wan Liu; Yan Sun; XuCheng Wang; Lin Yang; Zhenrong Zheng

doi:10.1117/12.2537957

18 November 2019 Monocular depth estimation based on unsupervised learning

Wan Liu, Yan Sun, XuCheng Wang, Lin Yang, Zhenrong Zheng

Author Affiliations +

Proceedings Volume 11187, Optoelectronic Imaging and Multimedia Technology VI; 1118704 (2019) https://doi.org/10.1117/12.2537957
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

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.

Citation Download Citation

Wan Liu, Yan Sun, XuCheng Wang, Lin Yang, and Zhenrong Zheng "Monocular depth estimation based on unsupervised learning", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 1118704 (18 November 2019); https://doi.org/10.1117/12.2537957

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE