This paper addresses the challenge of dense pixel correspondence estimation
between two images. This problem is closely related to optical flow estimation
task where ConvNets (CNNs) have recently achieved significant progress. While
optical flow methods produce very accurate results for the small pixel
translation and limited appearance variation scenarios, they hardly deal with
the strong geometric transformations that we consider in this work. In this
paper, we propose a coarse-to-fine CNN-based framework that can leverage the
advantages of optical flow approaches and extend them to the case of large
transformations providing dense and subpixel accurate estimates. It is trained
on synthetic transformations and demonstrates very good performance to unseen,
realistic, data. Further, we apply our method to the problem of relative camera
pose estimation and demonstrate that the model outperforms existing dense
approaches.

本研究针对两幅图像之间密集像素对应估计的挑战进行探讨，提出了一种基于粗到细的 CNN 框架，能够扩展光流方法的优势，解决大范围变换并提供密集和亚像素准确估计的问题，并在相对摄像机姿态估计问题上表现优异。