论文标题
双分辨率对应网络
Dual-Resolution Correspondence Networks
论文作者
论文摘要
我们解决了一对图像之间建立密集像素对应的问题。在这项工作中，我们介绍了双分辨率对应网络（DualRC-NET），以粗到1的方式获得像素的对应关系。 DualRC-NET提取了粗分辨率和精细分辨率特征图。粗图用于产生完整但粗糙的4D相关张量，然后通过可学习的邻居共识模块来完善。精细分辨率特征图用于获得由精制的粗4D相关张量引导的最终致密对应。选定的粗分辨率匹配分数允许精细分辨率功能仅专注于有限数量的置信度有限的匹配。通过这种方式，DualRC-NET急剧提高了匹配的可靠性和本地化精度，同时避免将昂贵的4D卷积内核应用于精细分辨率特征图。我们全面评估了我们的方法，包括HPATCHES，INLOC和ACACHEN DAY NITH，包括HPATCHES，INLOC和ACHEN夜晚。它在所有这些方面都取得了最新的结果。
We tackle the problem of establishing dense pixel-wise correspondences between a pair of images. In this work, we introduce Dual-Resolution Correspondence Networks (DualRC-Net), to obtain pixel-wise correspondences in a coarse-to-fine manner. DualRC-Net extracts both coarse- and fine- resolution feature maps. The coarse maps are used to produce a full but coarse 4D correlation tensor, which is then refined by a learnable neighbourhood consensus module. The fine-resolution feature maps are used to obtain the final dense correspondences guided by the refined coarse 4D correlation tensor. The selected coarse-resolution matching scores allow the fine-resolution features to focus only on a limited number of possible matches with high confidence. In this way, DualRC-Net dramatically increases matching reliability and localisation accuracy, while avoiding to apply the expensive 4D convolution kernels on fine-resolution feature maps. We comprehensively evaluate our method on large-scale public benchmarks including HPatches, InLoc, and Aachen Day-Night. It achieves the state-of-the-art results on all of them.
