DMHomo: Learning Homography with Diffusion Models

IF 7.8 1区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Haipeng Li, Hai Jiang, Ao Luo, Ping Tan, Haoqiang Fan, Bing Zeng, Shuaicheng Liu
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引用次数: 0

Abstract

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 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: https://github.com/lhaippp/DMHomo

DMHomo:利用扩散模型学习同构模型
由于缺乏足够的标记训练数据,有监督的同源性估计方法面临着挑战。为了解决这个问题,我们提出了基于扩散模型的监督同源性学习框架 DMHomo。该框架生成的图像对具有准确的标签、逼真的图像内容和逼真的间隔运动,确保它们满足充分的图像对要求。我们利用从现有方法中计算出的带有伪标签(如同构图和优势平面掩码)的无标签图像对来训练扩散模型,从而生成一个有监督的训练数据集。为了进一步提高性能,我们引入了一种新的概率掩码损失(通过监督训练识别离群区域)和一种迭代机制,以连续优化生成模型和同构模型。实验结果表明,DMHomo 有效克服了监督同构学习中合格数据集稀缺的问题,并提高了对真实场景的泛化能力。代码和数据集可在以下网址获取: https://github.com/lhaippp/DMHomo
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Graphics
ACM Transactions on Graphics 工程技术-计算机:软件工程
CiteScore
14.30
自引率
25.80%
发文量
193
审稿时长
12 months
期刊介绍: ACM Transactions on Graphics (TOG) is a peer-reviewed scientific journal that aims to disseminate the latest findings of note in the field of computer graphics. It has been published since 1982 by the Association for Computing Machinery. Starting in 2003, all papers accepted for presentation at the annual SIGGRAPH conference are printed in a special summer issue of the journal.
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