论文标题
重新访问街道到意见图像地理位置定位和方向估计
Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation
论文作者
论文摘要
在参考集中，将查询街道视图图像与GPS标记的空中图像匹配的街道到意见图像地理位置化，最近引起了人们越来越多的关注。在本文中，我们重新审视了这个问题，并指出了有关图像对齐信息的忽略问题。我们表明，简单的暹罗网络的性能高度依赖于对齐设置，如果以前的作品具有不同的假设，对先前作品的比较可能是不公平的。我们没有在对齐假设下专注于特征提取，而是表明，无论对齐方式如何，公制学习技术的改进都会显着提高性能。在不利用对齐信息的情况下，我们的管道在全景和裁剪数据集上都优于以前的作品。此外，我们进行可视化，以帮助使用Grad-CAM了解学习模型和对齐信息的效果。通过发现近似旋转不变的激活图，我们提出了一种新的方法，以估计一对带有未知对齐信息的跨视图图像之间的方向/对齐方式。它在CVUSA数据集上实现了最新结果。
Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently. In this paper, we revisit this problem and point out the ignored issue about image alignment information. We show that the performance of a simple Siamese network is highly dependent on the alignment setting and the comparison of previous works can be unfair if they have different assumptions. Instead of focusing on the feature extraction under the alignment assumption, we show that improvements in metric learning techniques significantly boost the performance regardless of the alignment. Without leveraging the alignment information, our pipeline outperforms previous works on both panorama and cropped datasets. Furthermore, we conduct visualization to help understand the learned model and the effect of alignment information using Grad-CAM. With our discovery on the approximate rotation-invariant activation maps, we propose a novel method to estimate the orientation/alignment between a pair of cross-view images with unknown alignment information. It achieves state-of-the-art results on the CVUSA dataset.
