关键词: 深度估计, 生成对抗网络, 图像转换, 半监督学习, 深度学习

Abstract:

The Generative Adversarial Network（GAN） has a low accuracy rate in the depth estimation task in outdoor scenes, it is inaccurate for object boundary judgment. Focusing on this problem, this paper proposes a monocular depth estimation algorithm based on Cycle Generation Adversarial Network（CycleGAN）. The algorithm splits the process of mapping a single image to a depth image into two sub-stages. In the first stage, the network learns the basic spatial characteristics of the image to obtain a depthmap at a coarse scale. On the basis of the former, the second stage optimizes the depthmap by comparing the differences in details to obtain a depthmap at a fine scale. In order to further improve the accuracy of depth estimation, the L1 distance is introduced into the loss function, so that the network can learn the pixel-to-pixel mapping relationship and avoid large deviations and distortions. Experimental results on the public outdoor scene dataset Make3D show that, compared with similar algorithms, this algorithm achieve better results in average relative error and root mean square error.

Key words: depth estimation, Generative Adversarial Network（GAN）, image conversion, semi-supervised learning, deep learning