Supervised Small-Baseline and Large-Baseline Homography Learning With Diffusion-Based Data Generation

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 for supervised small-baseline and large-baseline homography learning and yield a state-of-the-art homography estimation 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 refinement diffusion model. 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 outperforms existing competitors and previous supervised methods can also be improved based on the generated dataset.