Current self-supervised methods for monocular depth estimation are largely
based on deeply nested convolutional networks that leverage stereo image pairs
or monocular sequences during a training phase. However, they often exhibit
inaccurate results around occluded regions and depth boundaries. In this paper,
we present a simple yet effective approach for monocular depth estimation using
stereo image pairs. The study aims to propose a student-teacher strategy in
which a shallow student network is trained with the auxiliary information
obtained from a deeper and more accurate teacher network. Specifically, we
first train the stereo teacher network by fully utilizing the binocular
perception of 3-D geometry and then use the depth predictions of the teacher
network to train the student network for monocular depth inference. This
enables us to exploit all available depth data from massive unlabeled stereo
pairs. We propose a strategy that involves the use of a data ensemble to merge
the multiple depth predictions of the teacher network to improve the training
samples by collecting non-trivial knowledge beyond a single prediction. To
refine the inaccurate depth estimation that is used when training the student
network, we further propose stereo confidence-guided regression loss that
handles the unreliable pseudo depth values in occlusion, texture-less region,
and repetitive pattern. To complement the existing dataset comprising outdoor
driving scenes, we built a novel large-scale dataset consisting of one million
outdoor stereo images taken using hand-held stereo cameras. Finally, we
demonstrate that the monocular depth estimation network provides feature
representations that are suitable for high-level vision tasks. The experimental
results for various outdoor scenarios demonstrate the effectiveness and
flexibility of our approach, which outperforms state-of-the-art approaches.

使用基于学生 - 教师策略、数据集成和立体信心引导回归损失的方法，可以提高单目深度估计的精度，并用于高级视觉任务，如室外场景的驾驶情境等。

用于半监督单目深度估计的 RGB-D 大型数据集

A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation

There is an increasing interest on accelerating neural networks for real-time
applications. We study the student-teacher strategy, in which a small and fast
student network is trained with the auxiliary information learned from a large
and accurate teacher network. We propose to use conditional adversarial
networks to learn the loss function to transfer knowledge from teacher to
student. The proposed method is particularly effective for relatively small
student networks. Moreover, experimental results show the effect of network
size when the modern networks are used as student. We empirically study the
trade-off between inference time and classification accuracy, and provide
suggestions on choosing a proper student network.

本文介绍了使用条件对抗网络来学习从大型精准教师神经网络中获取知识来训练小型快速学生神经网络的学生 - 教师策略，并研究了网络大小对于分类准确度和推理时间之间的平衡的影响和建议。