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.
翻译:建立两个图像之间的对应关系是计算机视觉的一个重要研究方向。 当估计两个图像之间的关系时, 它经常受到外部线的干扰。 在本文中, 我们提议一个能够过滤外部线噪音的进化神经网络。 它可以输出两个特征点的概率, 代表相机相对面貌的基本矩阵的概率。 外部线主要是由先前的处理过程带来的噪音造成的。 外部线的拒绝可以被视为消除噪音的问题, 而软门槛功能对减少噪音有很好的影响。 因此, 我们设计了一个基于软门槛功能的适应性分解模块, 以去除外部线的噪音组件, 以降低外线预测为内向的概率。 YFCC100M 数据集的实验结果显示, 我们的方法超过了相对估计的状态。