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/JianghaiSCU/RealSH.

翻译：本文提出一种迭代框架，包含生成阶段与训练阶段两个环节，用于生成真实训练数据并构建监督式单应性网络。在生成阶段，针对未标注图像对，我们利用预估计的主平面掩码及其单应性，结合作为真值的另一采样单应性，生成具有真实运动形态的新标注训练对。在训练阶段，通过内容一致性模块与质量评估模块对生成数据进行优化，并将其用于监督式单应性网络的训练。每次迭代完成后，训练所得网络将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，数据集质量与网络性能可实现渐进式同步提升。实验结果表明，我们的方法达到了最优性能，且现有监督式方法亦可基于所生成数据集获得改进。代码与数据集详见https://github.com/JianghaiSCU/RealSH。