XFeat: Accelerated Features for Lightweight Image Matching

We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamen-tal design choices in convolutional neural networks for de-tecting, extracting, and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular, accu-rate image matching requires sufficiently large image res-olutions -for this reason, we keep the resolution as large as possible while limiting the number of channels in the net-work. Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more…