RF-net: An end-to-end image matching network based on receptive field
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.
Publication Source (Journal or Book title)
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Shen, X., Wang, C., Li, X., Yu, Z., Li, J., Wen, C., Cheng, M., & He, Z. (2019). RF-net: An end-to-end image matching network based on receptive field. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 8124-8132. https://doi.org/10.1109/CVPR.2019.00832