论文标题
SSORN：用于强大同构估计的自我监督的离群拆除网络
SSORN: Self-Supervised Outlier Removal Network for Robust Homography Estimation
论文作者
论文摘要
传统同型估计管道包括四个主要步骤：特征检测，特征匹配，离群拆卸和转换估计。最近的深度学习模型旨在使用单个卷积网络解决同型估计问题。尽管这些模型以端到端的方式进行了培训以简化同型估计问题，但它们缺乏功能匹配步骤和/或离群拆卸步骤，这是传统同型估计管道中的重要步骤。在本文中，我们试图建立一个深度学习模型，该模型模仿传统同型估计管道中的所有四个步骤。特别是，使用成本量技术实现功能匹配步骤。为了删除成本量的异常值，我们将此离群值的删除问题视为一个降解问题，并提出了一种新颖的自我监督损失来解决该问题。关于合成和真实数据集的广泛实验表明，所提出的模型的表现优于现有的深度学习模型。
The traditional homography estimation pipeline consists of four main steps: feature detection, feature matching, outlier removal and transformation estimation. Recent deep learning models intend to address the homography estimation problem using a single convolutional network. While these models are trained in an end-to-end fashion to simplify the homography estimation problem, they lack the feature matching step and/or the outlier removal step, which are important steps in the traditional homography estimation pipeline. In this paper, we attempt to build a deep learning model that mimics all four steps in the traditional homography estimation pipeline. In particular, the feature matching step is implemented using the cost volume technique. To remove outliers in the cost volume, we treat this outlier removal problem as a denoising problem and propose a novel self-supervised loss to solve the problem. Extensive experiments on synthetic and real datasets demonstrate that the proposed model outperforms existing deep learning models.
