This paper proposes a new end-to-end trainable matching network based on
receptive field, RF-Net, to compute sparse correspondence between images.
Building end-to-end trainable matching framework is desirable and challenging.
The very recent approach, LF-Net, successfully embeds the entire feature
extraction pipeline into a jointly trainable pipeline, and produces the
state-of-the-art matching results. This paper introduces two modifications to
the structure of LF-Net. First, we propose to construct receptive feature maps,
which lead to more effective keypoint detection. Second, we introduce a general
loss function term, neighbor mask, to facilitate training patch selection. This
results in improved stability in descriptor training. We trained RF-Net on the
open dataset HPatches, and compared it with other methods on multiple benchmark
datasets. Experiments show that RF-Net outperforms existing state-of-the-art
methods.

本文提出一种新的端到端可训练的匹配网络 RF-Net，基于感受野计算图像之间的稀疏对应关系。通过引入感受野来构建特征图，增强关键点检测效果，同时加入邻居掩码来提高描述符训练的稳定性。我们在公开数据集 HPatches 上训练并与其他方法进行了对比，实验结果表明 RF-Net 在多个基准数据集上优于现有的最先进方法。