RoMa: Robust Dense Feature Matching

Figure 1 . RoMa is robust, i.e., able to match under extreme changes. We propose RoMa, a model for dense feature matching that is robust to a wide variety of challenging real-world changes in scale, illumination, viewpoint, and texture. We show correspondences estimated by RoMa on the extremely challenging benchmark WxBS [37] , where most previous methods fail, and on which we set a new state-of-the-art with an improvement of 36% mAA. The estimated correspondences are visualized by grid sampling coordinates bilinearly from the other image, using the estimated warp, and multiplying with the estimated confidence.