LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

TL;DR

LiteFlowNet is a compact, efficient CNN that outperforms larger models like FlowNet2 in optical flow estimation, achieving higher accuracy with significantly fewer parameters and faster processing speeds.

Contribution

The paper introduces a lightweight CNN architecture with novel flow inference, regularization, and feature extraction techniques that improve accuracy and efficiency over existing models.

Findings

Outperforms FlowNet2 on Sintel and KITTI benchmarks.

Achieves 30 times smaller model size and 1.36 times faster speed.

Provides state-of-the-art accuracy with fewer parameters.

Abstract

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 https://github.com/twhui/LiteFlowNet .