FlowIt: Global Matching for Optical Flow with Confidence-Guided Refinement

• URL: http://arxiv.org/abs/2603.28759v1
• Date: Mon, 30 Mar 2026 17:58:12 GMT
• Title: FlowIt: Global Matching for Optical Flow with Confidence-Guided Refinement
• Abstract summary: FlowIt is a novel architecture for optical flow estimation.<n>It is designed to robustly handle large pixel displacements.<n>It achieves state-of-the-art results on the competitive Sintel and KITTI benchmarks.
• Score: 44.07406912385784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. These cues are then seamlessly integrated into a guided refinement stage, where the network actively propagates reliable motion estimates from high-confidence regions into ambiguous, low-confidence areas. Extensive experiments across the Sintel, KITTI, Spring, and LayeredFlow datasets validate the efficacy of our approach. FlowIt achieves state-of-the-art results on the competitive Sintel and KITTI benchmarks, while simultaneously establishing new state-of-the-art cross-dataset zero-shot generalization performance on Sintel, Spring, and LayeredFlow.

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