XFeat: Accelerated Features for Lightweight Image Matching

arXiv:2404.19174v1 Announce Type: new
Abstract: 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 …

abstract algorithms architecture arxiv convolutional convolutional neural networks cs.cv design devices features fundamental image networks neural networks robust type visual