Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (12): 12-16.doi: 10.16180/j.cnki.issn1007-7820.2020.12.003

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A Domain Adaptive Depth Estimation Method for Structural Perception Loss

ZHAN Yan,ZHANG Juan   

  1. College of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2019-10-08 Online:2020-12-15 Published:2020-12-22
  • Supported by:
    National Natural Science Foundation of China(61772328)


In the domain adaptive depth estimation method, the structural differences between domain images are large. In view of this problem, a domain adaptive depth estimation method for structural perceptual loss is proposed. This method uses pre-trained convolutional neural networks to extract features from images, and measures structural similarity on features, which reduces the difference between domain images and improved the stability of the transform module. This method uses synthetic image depth pairs and real image training, and eliminates the requirement of depth labels and physical geometric information for real images. Experiments on the KITTI dataset achieve a depth accuracy rate of 96.6%, which proves that the method can effectively improve the depth accuracy.

Key words: depth estimation, image processing, monocular image, perceptual loss, domain adaptation, structural similarity index

CLC Number: 

  • TP751