The KITTI Vision Benchmark Suite

Dense optical flow estimation plays a key role in
many robotic vision tasks. It has been predicted
with satisfying accuracy than traditional methods
with advent of deep learning. However, current
networks often occupy large number of parameters
and require heavy computation costs. These
drawbacks have hindered applications on power- or
memory-constrained mobile devices. To deal with
these challenges, in this paper, we dive into
designing efficient structure for fast and
accurate optical flow prediction. Our proposed
FastFlowNet works in the well-known coarse-to-fine
manner with innovations of head enhanced pooling
pyramid (HEPP) feature extractor, center dense
dilated correlation (CDDC) layer and efficient
shuffle block decoder (SBD). Experiments on both
synthetic Sintel and real-world KITTI datasets
demonstrate the effectiveness of proposed
approaches, which consumes only 1/10 computation
of comparable networks to get 90% of their
performance.

FastFlowNet

@InProceedings{Kong_2021_ICRA,
author={Kong, Lingtong and Shen, Chunhua and Yang,
Jie},
title={FastFlowNet: A Lightweight Network for Fast
Optical Flow Estimation},
booktitle={2021 IEEE International Conference on
Robotics and Automation (ICRA)},
year={2021}
}