AI RESEARCH
FlowIt: Global Matching for Optical Flow with Confidence-Guided Refinement
arXiv CS.CV
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ArXi:2603.28759v1 Announce Type: new We present FlowIt, a novel architecture for optical flow estimation designed to robustly handle large pixel displacements. At its core, FlowIt leverages a hierarchical transformer architecture that captures extensive global context, enabling the model to effectively model long-range correspondences. To overcome the limitations of localized matching, we formulate the flow initialization as an optimal transport problem. This formulation yields a highly robust initial flow field, alongside explicitly derived occlusion and confidence maps.