Data-driven depth estimation methods struggle with the generalization outside
their training scenes due to the immense variability of the real-world scenes.
This problem can be partially addressed by utilising synthetically generated
images, but closing the synthetic-real domain gap is far from trivial. In this
paper, we tackle this issue by using domain invariant defocus blur as direct
supervision. We leverage defocus cues by using a permutation invariant
convolutional neural network that encourages the network to learn from the
differences between images with a different point of focus. Our proposed
network uses the defocus map as an intermediate supervisory signal. We are able
to train our model completely on synthetic data and directly apply it to a wide
range of real-world images. We evaluate our model on synthetic and real
datasets, showing compelling generalization results and state-of-the-art depth
prediction.

本文通过使用领域不变的虚焦模糊作为直接监督来解决数据驱动的深度估计方法在不训练场景外的概括能力问题，并利用置换不变卷积神经网络利用虚焦提示，通过不同焦点的图像差异训练模型，从而在合成数据集合和真实世界图像集合上取得了令人信服的泛化结果和最先进的深度预测。

聚焦模糊：连接深度估计合成与实际领域的差距

Focus on defocus: bridging the synthetic to real domain gap for depth  estimation

Leveraging synthetically rendered data offers great potential to improve
monocular depth estimation and other geometric estimation tasks, but closing
the synthetic-real domain gap is a non-trivial and important task. While much
recent work has focused on unsupervised domain adaptation, we consider a more
realistic scenario where a large amount of synthetic training data is
supplemented by a small set of real images with ground-truth. In this setting,
we find that existing domain translation approaches are difficult to train and
offer little advantage over simple baselines that use a mix of real and
synthetic data. A key failure mode is that real-world images contain novel
objects and clutter not present in synthetic training. This high-level domain
shift isn't handled by existing image translation models.
Based on these observations, we develop an attention module that learns to
identify and remove difficult out-of-domain regions in real images in order to
improve depth prediction for a model trained primarily on synthetic data. We
carry out extensive experiments to validate our attend-remove-complete approach
(ARC) and find that it significantly outperforms state-of-the-art domain
adaptation methods for depth prediction. Visualizing the removed regions
provides interpretable insights into the synthetic-real domain gap.

通过注意力模块，学习识别和去除真实图像中的困难部分，以提高合成数据训练的模型对真实图像的景深估计。