Dense optical flow can be estimated accurately in one forward pass by combining DINO-v2 semantic priors and monocular depth geometric cues via global matching, reaching 2.81 EPE on Sintel Final without any refinement.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
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Rethinking Dense Optical Flow without Test-Time Scaling
Dense optical flow can be estimated accurately in one forward pass by combining DINO-v2 semantic priors and monocular depth geometric cues via global matching, reaching 2.81 EPE on Sintel Final without any refinement.