Deep learning methods for optical flow estimation usually increase the receptive field of convolution through reducing image resolution, which results in loss of spatial detail information during feature extraction. In this paper, we introduce dilated convolution into feature pyramid network, which can extract multi-scale features containing more motion details and can further improve the accuracy of optical flow estimation. The unsupervised loss function is based on forward–backward consistency check and robust census transform that has a good constraint performance in the case of illumination changes, which can train an unsupervised learning optical flow model with higher accuracy. Our network is trained on FlyingChairs and KITTI raw datasets with an unsupervised manner and tested on MPI-Sintel, KITTI 2012 and KITTI 2015 benchmarks. The experimental results show the advantages of our method in unsupervised learning approaches.

用于光流估计的深度学习方法通​​常通过降低图像分辨率来增加卷积的接收场，从而导致特征提取期间空间细节信息的丢失。在本文中，我们将膨胀卷积引入特征金字塔网络中，该网络可以提取包含更多运动细节的多尺度特征，并可以进一步提高光流估计的准确性。无监督损失函数基于前向后一致性检查和鲁棒的人口普查变换，在光照变化的情况下具有良好的约束性能，可以以更高的精度训练无监督学习光流模型。我们的网络在无人值守的情况下接受了FlyingChairs和KITTI原始数据集的培训，并在MPI-Sintel，KITTI 2012和KITTI 2015基准上进行了测试。