论文标题
fn-net：通过过滤噪声去除离群值
FN-Net:Remove the Outliers by Filtering the Noise
论文作者
论文摘要
建立两个图像之间的对应关系是计算机视觉的重要研究方向。当估计两个图像之间的关系时，通常会受到异常值的干扰。在本文中，我们提出了一个卷积神经网络，该网络可以过滤异常值的噪声。它可以输出一对特征点是一个嵌入的概率，并回归代表相机相对姿势的必需矩阵。离群值主要是由先前处理引入的噪声引起的。异常值拒绝可以视为消除噪声的问题，并且软阈值功能对降低降噪作用非常好。因此，我们设计了一个基于软阈值函数的自适应denoising模块，以删除离群值中的噪声组件，以降低预测离群值的概率。 YFCC100M数据集的实验结果表明，我们的方法超过了相对姿势估计的最新方法。
Establishing the correspondence between two images is an important research direction of computer vision. When estimating the relationship between two images, it is often disturbed by outliers. In this paper, we propose a convolutional neural network that can filter the noise of outliers. It can output the probability that the pair of feature points is an inlier and regress the essential matrix representing the relative pose of the camera. The outliers are mainly caused by the noise introduced by the previous processing. The outliers rejection can be treated as a problem of noise elimination, and the soft threshold function has a very good effect on noise reduction. Therefore, we designed an adaptive denoising module based on soft threshold function to remove noise components in the outliers, to reduce the probability that the outlier is predicted to be an inlier. Experimental results on the YFCC100M dataset show that our method exceeds the state-of-the-art in relative pose estimation.
