Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Encoder–decoder with densely convolutional networks for monocular depth estimation

Not Accessible

Your library or personal account may give you access

Abstract

We propose an encoder–decoder with densely convolutional networks model to recover the depth information from a single RGB image without the need for depth sensors. The encoder part serves to extract the most representative information from the original data through a series of convolution operations and to reduce the resolution of the spatial input feature. We use the decoder section to produce an upsampling structure that improves the output resolution. Our model is trained from scratch, without any special tuning process, and uses a new optimization function to adaptively learn the rate. We demonstrate the effectiveness of the method by evaluating both indoor and outdoor scenes, and the experimental results show that our proposed approach is more accurate than competing methods.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
Joint estimation of depth and motion from a monocular endoscopy image sequence using a multi-loss rebalancing network

Shiyuan Liu, Jingfan Fan, Dengpan Song, Tianyu Fu, Yucong Lin, Deqiang Xiao, Hong Song, Yongtian Wang, and Jian Yang
Biomed. Opt. Express 13(5) 2707-2727 (2022)

Self-supervised stereo depth estimation based on bi-directional pixel-movement learning

Huachun Wang, Xinzhu Sang, Duo Chen, Peng Wang, Xiaoqian Ye, Shuai Qi, and Binbin Yan
Appl. Opt. 61(7) D7-D14 (2022)

Panoramic depth estimation via supervised and unsupervised learning in indoor scenes

Keyang Zhou, Kailun Yang, and Kaiwei Wang
Appl. Opt. 60(26) 8188-8197 (2021)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (6)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (7)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Metrics

Select as filters


Select Topics Cancel
© Copyright 2022 | Optica Publishing Group. All Rights Reserved