论文标题
专注于散焦：将合成到真实域间隙桥接以进行深度估计
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
论文作者
论文摘要
由于现实世界的巨大可变性，数据驱动的深度估计方法与训练场景之外的概括相斗争。可以通过利用合成生成的图像来部分解决此问题，但是闭合合成域间隙远非微不足道。在本文中，我们通过使用域不变的散焦模糊作为直接监督来解决此问题。我们通过使用置换不变的卷积神经网络来利用散焦提示，该卷积神经网络鼓励网络从具有不同焦点的图像之间的差异中学习。我们提出的网络使用Defocus Map作为中间监督信号。我们能够将模型完全训练在合成数据上，并将其直接应用于广泛的现实图像。我们在合成和真实数据集上评估了我们的模型，显示了引人入胜的概括结果和最新的深度预测。
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.
