Multimedia Tools and Applications

, Volume 76, Issue 18, pp 18585–18604 | Cite as

Depth estimation from single monocular images using deep hybrid network

  • Aleksei Grigorev
  • Feng Jiang
  • Seungmin Rho
  • Worku J. Sori
  • Shaohui Liu
  • Sergey Sai
Article

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.

Keywords

CNN LSTM Depth estimation Monocular image RNN 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Aleksei Grigorev
    • 1
    • 2
  • Feng Jiang
    • 1
  • Seungmin Rho
    • 3
  • Worku J. Sori
    • 1
  • Shaohui Liu
    • 1
  • Sergey Sai
    • 2
  1. 1.Department Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Computer EngineeringPacific National UniversityKhabarovskRussia
  3. 3.Department of Media SoftwareSungkyul UniversityAnyangSouth Korea

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