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Optical flow estimation is an essential step for many real-world computervision tasks. Existing deep networks have achieved satisfactory results bymostly employing a pyramidal coarse-to-fine paradigm, where a key process is toadopt warped target feature based on previous flow prediction to correlate withsource feature for building 3D matching cost volume. However, the warpingoperation can lead to troublesome ghosting problem that results in ambiguity.Moreover, occluded areas are treated equally with non occluded regions in mostexisting works, which may cause performance degradation. To deal with thesechallenges, we propose a lightweight yet efficient optical flow network, namedOAS-Net (occlusion aware sampling network) for accurate optical flow. First, anew sampling based correlation layer is employed without noisy warpingoperation. Second, a novel occlusion aware module is presented to make raw costvolume conscious of occluded regions. Third, a shared flow and occlusionawareness decoder is adopted for structure compactness. Experiments on Sinteland KITTI datasets demonstrate the effectiveness of proposed approaches.

OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow