Zero-Shot DINOv3-Based Image Matching via Many-to-Many Association

We explore zero-shot image matching based on DINOv3 features. These general-purpose features are semantically rich yet geometrically ambiguous -- the nearest neighbor in feature space is often a false geometric match -- so the conventional one-to-one association paradigm is prone to discarding true correspondences. We instead retain many-to-many associations and cast the ensuing estimation as maximum-likelihood inference. This formulation reveals the existing robust mechanism for many-to-many association as a zeroth-order approximation to the otherwise intractable likelihood, and yields a novel mechanism with faster evaluation and finer granularity. We condense these theoretical findings into a two-stage LO-RANSAC algorithm which, combined with careful layer selection and positional bias correction for DINOv3 features, forms a zero-shot pipeline that degrades gracefully under background variation and across RGB-thermal pairs, where specialized baselines collapse under domain shift. Code released at https://github.com/LIAS-CUHKSZ/Zero-Shot-DINOv3-Image-Matching.