  Feature matching is an important computer vision task that involves
estimating correspondences between two images of a 3D scene, and dense methods
estimate all such correspondences. The aim is to learn a robust model, i.e., a
model able to match under challenging real-world changes. In this work, we
propose such a model, leveraging frozen pretrained features from the foundation
model DINOv2. Although these features are significantly more robust than local
features trained from scratch, they are inherently coarse. We therefore combine
them with specialized ConvNet fine features, creating a precisely localizable
feature pyramid. To further improve robustness, we propose a tailored
transformer match decoder that predicts anchor probabilities, which enables it
to express multimodality. Finally, we propose an improved loss formulation
through regression-by-classification with subsequent robust regression. We
conduct a comprehensive set of experiments that show that our method, RoMa,
achieves significant gains, setting a new state-of-the-art. In particular, we
achieve a 36% improvement on the extremely challenging WxBS benchmark. Code is
provided at https://github.com/Parskatt/RoMa

Computer Vision and Pattern Recognition