The KITTI Vision Benchmark Suite

Significant progress has been made for estimating
optical flow using deep neural networks. Advanced
deep models achieve accurate flow estimation
often with a considerable computation complexity
and time-consuming training processes. In this
work, we present a lightweight yet effective
model for real-time optical flow estimation,
termed FDFlowNet (fast deep flownet). We achieve
better or similar accuracy on the challenging
KITTI and Sintel benchmarks while being about 2
times faster than PWC-Net. This is achieved by a
carefully-designed structure and newly proposed
components. We first introduce an U-shape network
for constructing multi-scale feature which
benefits upper levels with global receptive field
compared with pyramid network. In each scale, a
partial fully connected structure with dilated
convolution is proposed for flow estimation that
obtains a good balance among speed, accuracy and
number of parameters compared with sequential
connected and dense connected structures.
Experiments demonstrate that our model achieves
state-of-the-art performance while being fast and
lightweight.

FDFlowNet

@inproceedings{kong2020fdflownet,
author={Lingtong Kong and Jie Yang},
booktitle={IEEE International Conference on
Image Processing (ICIP)},
title={FDFlowNet: Fast Optical Flow Estimation
using a Deep Lightweight Network},
year={2020},}