一种结构感知损失的域适应深度估计方法

doi:10.16180/j.cnki.issn1007-7820.2020.12.003

Abstract

Abstract:

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

ZHAN Yan,ZHANG Juan. A Domain Adaptive Depth Estimation Method for Structural Perception Loss[J].Electronic Science and Technology, 2020, 33(12): 12-16.

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References 16

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方法	数据集	Abs	SQ	RMSE	RMSE(log)	δ<1.25³	δ<1.25²	δ<1.25³
文献[3]	K(I,D)	0.190	1.515	7.156	0.270	0.692	0.899	0.958
文献[4]	K(L,R)	0.169	1.080	5.104	0.273	0.740	0.904	0.962
文献[5]	CS,K(L,R)	0.117	0.762	3.972	0.206	0.860	0.948	0.976
文献[6]	V(I,D), K(I)	0.169	1.230	4.717	0.245	0.769	0.912	0.965
基准	V(I,D)	0.521	11.024	10.598	0.473	0.638	0.755	0.835
本文	V(I,D), K(I)	0.167	1.240	4.715	0.230	0.772	0.914	0.966

A Domain Adaptive Depth Estimation Method for Structural Perception Loss

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