In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network. In the generation phase, given an unlabeled image pair, we utilize the pre-estimated dominant plane masks and homography of the pair, along with another sampled homography that serves as ground truth to generate a new labeled training pair with realistic motion. In the training phase, the generated data is used to train the supervised homography network, in which the training data is refined via a content consistency module and a quality assessment module. Once an iteration is finished, the trained network is used in the next data generation phase to update the pre-estimated homography. Through such an iterative strategy, the quality of the dataset and the performance of the network can be gradually and simultaneously improved. Experimental results show that our method achieves state-of-the-art performance and existing supervised methods can be also improved based on the generated dataset. Code and dataset are available at https://github.com/megvii-research/RealSH.

翻译：本文提出了一种迭代框架，该框架包含两个阶段：生成阶段和训练阶段，用于生成真实的训练数据并训练出一个监督式单应性网络。在生成阶段，给定一对无标注图像，我们利用预估计的主平面掩膜及其单应性，结合一个作为真实标签采样的额外单应性，生成具有真实运动的新标注训练对。在训练阶段，生成的数据被用于训练监督式单应性网络，其中训练数据通过内容一致性模块和质量评估模块进行优化。每次迭代完成后，训练好的网络被用于下一数据生成阶段，以更新预估计的单应性。通过这种迭代策略，数据集的质量和网络的性能可以逐步同步提升。实验结果表明，我们的方法达到了当前最优性能，且现有监督方法也能基于生成的数据集得到改进。代码和数据集可在 https://github.com/megvii-research/RealSH 获取。