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Data-driven depth estimation methods struggle with the generalization outsidetheir training scenes due to the immense variability of the real-world scenes.This problem can be partially addressed by utilising synthetically generatedimages, but closing the synthetic-real domain gap is far from trivial. In thispaper, we tackle this issue by using domain invariant defocus blur as directsupervision. We leverage defocus cues by using a permutation invariantconvolutional neural network that encourages the network to learn from thedifferences between images with a different point of focus. Our proposednetwork uses the defocus map as an intermediate supervisory signal. We are ableto train our model completely on synthetic data and directly apply it to a widerange of real-world images. We evaluate our model on synthetic and realdatasets, showing compelling generalization results and state-of-the-art depthprediction.

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