  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.
