Depth estimation from single monocular images using deep hybrid network

Depth estimation from single monocular images using deep hybrid network Depth estimation is a significant task in the robotics vision. In this paper, we address the depth estimation from a single monocular image, which is a challenging problem in automated vision systems since a single image alone does not carry any additional measurements. To tackle our main objective, we design a deep hybrid neural network, which is composed of convolutional and recurrent layers (ReNet), where each ReNet layer is composed of the Long Short-Term Memory unit (LSTM), which is famous for the ability to memorize long-range context. In the proposed network, ReNet layers aim to enrich the features representation by directly capturing global context. The effective integration of ReNet and convolutional layers in the common CNN framework allows us to train the hybrid network in the end-to-end fashion. Experimental evaluation on the benchmarks dataset demonstrated, that hybrid network achieves the state-of-the-art results without any post-processing steps. Moreover, the composition of recurrent and convolutional layers provide more satisfying results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Depth estimation from single monocular images using deep hybrid network

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-016-4200-x
Publisher site
See Article on Publisher Site

Abstract

Depth estimation is a significant task in the robotics vision. In this paper, we address the depth estimation from a single monocular image, which is a challenging problem in automated vision systems since a single image alone does not carry any additional measurements. To tackle our main objective, we design a deep hybrid neural network, which is composed of convolutional and recurrent layers (ReNet), where each ReNet layer is composed of the Long Short-Term Memory unit (LSTM), which is famous for the ability to memorize long-range context. In the proposed network, ReNet layers aim to enrich the features representation by directly capturing global context. The effective integration of ReNet and convolutional layers in the common CNN framework allows us to train the hybrid network in the end-to-end fashion. Experimental evaluation on the benchmarks dataset demonstrated, that hybrid network achieves the state-of-the-art results without any post-processing steps. Moreover, the composition of recurrent and convolutional layers provide more satisfying results.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Dec 20, 2016

References

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