  Optical flow estimation is an essential step for many real-world computer
vision tasks. Existing deep networks have achieved satisfactory results by
mostly employing a pyramidal coarse-to-fine paradigm, where a key process is to
adopt warped target feature based on previous flow prediction to correlate with
source feature for building 3D matching cost volume. However, the warping
operation can lead to troublesome ghosting problem that results in ambiguity.
Moreover, occluded areas are treated equally with non occluded regions in most
existing works, which may cause performance degradation. To deal with these
challenges, we propose a lightweight yet efficient optical flow network, named
OAS-Net (occlusion aware sampling network) for accurate optical flow. First, a
new sampling based correlation layer is employed without noisy warping
operation. Second, a novel occlusion aware module is presented to make raw cost
volume conscious of occluded regions. Third, a shared flow and occlusion
awareness decoder is adopted for structure compactness. Experiments on Sintel
and KITTI datasets demonstrate the effectiveness of proposed approaches.
