Supervised homography estimation methods face a challenge due to the lack of adequate labeled training data. To address this issue, we propose DMHomo, a diffusion model-based framework for supervised homography learning. This framework generates image pairs with accurate labels, realistic image content, and realistic interval motion, ensuring that they satisfy adequate pairs. We utilize unlabeled image pairs with pseudo labels such as homography and dominant plane masks, computed from existing methods, to train a diffusion model that generates a supervised training dataset. To further enhance performance, we introduce a new probabilistic mask loss, which identifies outlier regions through supervised training, and an iterative mechanism to optimize the generative and homography models successively. Our experimental results demonstrate that DMHomo effectively overcomes the scarcity of qualified datasets in supervised homography learning and improves generalization to real-world scenes. The code and dataset are available at GitHub ( https://github.com/lhaippp/DMHomo).

由于缺乏足够的标记训练数据，有监督的单应性估计方法面临着挑战。为了解决这个问题，我们提出了DMHomo，一种基于扩散模型的监督单应性学习框架。该框架生成具有准确标签、真实图像内容和真实间隔运动的图像对，确保它们满足足够的对。我们利用根据现有方法计算的具有伪标签（例如单应性和主平面掩模）的未标记图像对来训练生成监督训练数据集的扩散模型。为了进一步提高性能，我们引入了一种新的概率掩模损失，它通过监督训练来识别离群区域，以及一种迭代机制来连续优化生成模型和单应性模型。我们的实验结果表明，DMHomo 有效克服了监督单应性学习中合格数据集的稀缺性，并提高了对现实世界场景的泛化能力。代码和数据集可在 GitHub (https://github.com/lhaippp/DMHomo) 上获取。