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. There are two general principles to improve the design of FlowNet2 and SPyNet. The first principle is pyramidal feature extraction. The proposed network, dubbed Lite-FlowNet, consists of an encoder and a decoder. The encoder maps the given image pair, respectively, into two pyramids of multi-scale high-dimensional features. The decoder then
LiteFlowNet REF involves cascaded flow inference for flow warping and feature matching, and feature-driven local convolution (flconv) for flow regularization.
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science