FPCR-Net: Feature Pyramidal Correlation and Residual Reconstruction for Optical Flow Estimation

Optical flow estimation is an important yet challenging problem in the field of video analytics. The features of different semantics levels/layers of a convolutional neural network can provide information of different granularity. To exploit such flexible and comprehensive information, we propose a semi-supervised Feature Pyramidal Correlation and Residual Reconstruction Network (FPCR-Net) for optical flow estimation from frame pairs. It consists of two main modules: pyramid correlation mapping and residual reconstruction. The pyramid correlation mapping module takes advantage of the multi-scale correlations of global/local patches by aggregating features of different scales to form a multi-level cost volume. The residual reconstruction module aims to reconstruct the sub-band high-frequency residuals of finer optical flow in each stage. Based on the pyramid correlation mapping, we further propose a correlation-warping-normalization (CWN) module to efficiently exploit the correlation dependency. Experiment results show that the proposed scheme achieves the state-of-the-art performance, with improvement by 0.80, 1.15 and 0.10 in terms of average end-point error (AEE) against competing baseline methods - FlowNet2, LiteFlowNet and PWC-Net on the Final pass of Sintel dataset, respectively.