  We introduce a lightweight and accurate architecture for resource-efficient
visual correspondence. Our method, dubbed XFeat (Accelerated Features),
revisits fundamental design choices in convolutional neural networks for
detecting, extracting, and matching local features. Our new model satisfies a
critical need for fast and robust algorithms suitable to resource-limited
devices. In particular, accurate image matching requires sufficiently large
image resolutions - for this reason, we keep the resolution as large as
possible while limiting the number of channels in the network. Besides, our
model is designed to offer the choice of matching at the sparse or semi-dense
levels, each of which may be more suitable for different downstream
applications, such as visual navigation and augmented reality. Our model is the
first to offer semi-dense matching efficiently, leveraging a novel match
refinement module that relies on coarse local descriptors. XFeat is versatile
and hardware-independent, surpassing current deep learning-based local features
in speed (up to 5x faster) with comparable or better accuracy, proven in pose
estimation and visual localization. We showcase it running in real-time on an
inexpensive laptop CPU without specialized hardware optimizations. Code and
weights are available at www.verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24.
