The great potential of unsupervised monocular depth estimation has been
demonstrated by many works due to low annotation cost and impressive accuracy
comparable to supervised methods. To further improve the performance, recent
works mainly focus on designing more complex network structures and exploiting
extra supervised information, e.g., semantic segmentation. These methods
optimize the models by exploiting the reconstructed relationship between the
target and reference images in varying degrees. However, previous methods prove
that this image reconstruction optimization is prone to get trapped in local
minima. In this paper, our core idea is to guide the optimization with prior
knowledge from pretrained Flow-Net. And we show that the bottleneck of
unsupervised monocular depth estimation can be broken with our simple but
effective framework named FG-Depth. In particular, we propose (i) a flow
distillation loss to replace the typical photometric loss that limits the
capacity of the model and (ii) a prior flow based mask to remove invalid pixels
that bring the noise in training loss. Extensive experiments demonstrate the
effectiveness of each component, and our approach achieves state-of-the-art
results on both KITTI and NYU-Depth-v2 datasets.

本文介绍了一种名为 FG-Depth 的简单而有效的框架，通过利用预训练的 Flow-Net 的先验知识来引导优化，从而打破了无监督单眼深度估计的瓶颈，提出了流蒸馏损失和基于先验流的掩模来提高模型性能以及消除噪声，实验结果表明，该方法在 KITTI 和 NYU-Depth-v2 数据集上均达到了最领先的效果。