RoMa v2: Harder Better Faster Denser Feature Matching

Dense feature matching aims to estimate all correspondencesbetween two images of a 3D scene and has recently been established asthe gold standard due to its high accuracy and robustness. However,existing dense matchers still fail or perform poorly for many hard real-world scenarios, and high-precision models are often slow, limiting theirapplicability. In this paper, we attack these weaknesses on a wide frontthrough a series of systematic improvements that together yield a sig-nificantly better model. In particular, we construct a novel matchingarchitecture and loss, which, combined with a curated diverse trainingdistribution, enables our model to solve many complex matching tasks.We further make training faster through a decoupled two-stage matching-then-refinement pipeline, and at the same time, significantly reduce re-finement memory usage through a custom CUDA kernel. Finally, weleverage the recent DINOv3 foundation model along with multiple otherinsights to make the model more robust and unbiased. In our extensiveset of experiments, we show that the resulting novel matcher sets a newstate-of-the-art, being significantly more accurate than its predecessors.ω