While recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance, costly ground truth
annotations are required during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps and show that the depth estimation task can be effectively tackled
within an adversarial learning framework. Specifically, we propose a deep
generative network that learns to predict the correspondence field i.e. the
disparity map between two image views in a calibrated stereo camera setting.
The proposed architecture consists of two generative sub-networks jointly
trained with adversarial learning for reconstructing the disparity map and
organized in a cycle such as to provide mutual constraints and supervision to
each other. Extensive experiments on the publicly available datasets KITTI and
Cityscapes demonstrate the effectiveness of the proposed model and competitive
results with state of the art methods. The code and trained model are available
on this https URL

本文提出了一种基于对抗学习框架的无监督深度学习方法，用于预测深度图，通过学习预测校准立体相机设置下两个图像视图之间的对应场，提出了一个有两个生成子网络的架构，它们通过对抗训练共同用于重建视差图并相互约束和监督。实验结果表明，该模型可以有效地解决深度估计任务。