LiteFlowNet: A Lightweight Convolutional Neural Network
for Optical Flow Estimation
Abstract
FlowNet2 [14], 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 attains
performance on par with 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 github.com/twhui/LiteFlowNet