Abstract
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to learn depth estimation networks in an unsupervised way by exploring unsupervised cues, which are effective but less reliable than true labels. An emerging way to resolve this dilemma is to transfer knowledge from synthetic images with ground truth depth via domain adaptation techniques. However, these approaches overlook specific geometric structure of the natural images in the target domain (i.e., real data), which is important for high-performing depth prediction. Motivated by the observation, we propose a geometry-aware symmetric domain adaptation framework (GASDA) to explore the labels in the synthetic data and epipolar geometry in the real data jointly. Moreover, by training two image style translators and depth estimators symmetrically in an end-to-end network, our model achieves better image style transfer and generates high-quality depth maps. The experimental results demonstrate the effectiveness of our proposed method and comparable performance against the state-of-the-art. Code will be publicly available at: https://github.com/sshan-zhao/GASDA.
Abstract (translated)
监控深度估计由于采用了先进的深度网络体系结构，具有较高的精度。由于基态深度标签很难获得，最近的方法试图通过探索无监督的线索来无监督地学习深度估计网络，这是有效的，但不如真正的标签可靠。解决这一难题的一种新方法是通过领域自适应技术从具有地面真实深度的合成图像中传输知识。然而，这些方法忽略了目标域中自然图像的特定几何结构（即真实数据），这对于高性能深度预测很重要。在观测的激励下，我们提出了一个几何感知对称域适应框架（GASDA），以共同探索合成数据中的标签和真实数据中的极外几何。此外，通过在端到端网络中对称地训练两个图像样式转换器和深度估计器，我们的模型实现了更好的图像样式转换并生成高质量的深度图。实验结果表明，我们提出的方法的有效性和与最新技术相比较的性能。代码将在以下网址公开：https://github.com/sshan-zhao/gasda。
URL
https://arxiv.org/abs/1904.01870
