XFeat: Accelerated Features for Lightweight Image Matching
Authors: Guilherme Potje, Felipe Cadar, André Araujo, Renato Martins, Erickson R. Nascimento

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 verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24.