FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical
flow estimation, requires over 160M parameters to achieve accurate flow
estimation. In this paper we present an alternative network that outperforms
FlowNet2 on the challenging Sintel final pass and KITTI benchmarks, while being
30 times smaller in the model size and 1.36 times faster in the running speed.
This is made possible by drilling down to architectural details that might have
been missed in the current frameworks: (1) We present a more effective flow
inference approach at each pyramid level through a lightweight cascaded
network. It not only improves flow estimation accuracy through early
correction, but also permits seamless incorporation of descriptor matching in
our network. (2) We present a novel flow regularization layer to ameliorate the
issue of outliers and vague flow boundaries by using a feature-driven local
convolution. (3) Our network owns an effective structure for pyramidal feature
extraction and embraces feature warping rather than image warping as practiced
in FlowNet2. Our code and trained models are available at
this https URL .

本文介绍了一种新的神经网络，通过轻量级级联网络、流规则化层和特征提取的结构来提高光流估计的准确度，并且比现有的 FlowNet2 在模型大小和运行速度等方面都有所优化。