MotionFlow+: High-accuracy Optical Flow Estimation Network via Implicit Motion Prior and Uncertainty Guided Optimization

Optical flow estimation has made great progress, but still suffers from motion discontinuities. Although global matching and local regression have made significant efforts, explicit estimation and limited information tend to degrade the performance of optical flow estimation. To alleviate this issue, we propose a novel method named MotionFlow+ for high-accuracy optical flow estimation. First, we propose a cross-appearance enhancement module and an implicit motion prior block, which simultaneously enhance bidirectional features and generate robust motion prior information for the decoding process. Subsequently, we employ the occlusion-aware fusion module during the decoding stage to capture reliable global motion information from motion prior features and cost volume. Next, we align appearance features of the target frame with the reference frame through iteration flow to retrieve missing context crucial for motion decoding. Finally, we introduce an uncertainty-guided optimization module which utilizes uncertainty awareness to identify regions requiring optimization and perform targeted refinement of the iterative flow. Experimental results demonstrate the efficacy of our approach, achieving state-of-the-art performance, particularly outperforming most two-view methods on Sintel Final pass and KITTI-2015 online benchmarks.