FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation

by   Lingtong Kong, et al.

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 following innovations. First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed to intensify high-resolution pyramid feature while reducing parameters. Second, we introduce a novel center dense dilated correlation (CDDC) layer for constructing compact cost volume that can keep large search radius with reduced computation burden. Third, an efficient shuffle block decoder (SBD) is implanted into each pyramid level to acclerate flow estimation with marginal drops in accuracy. 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 only contains 1.37 M parameters and runs at 90 or 5.7 fps with one desktop NVIDIA GTX 1080 Ti or embedded Jetson TX2 GPU on Sintel resolution images.


page 1

page 3

page 4

page 5


OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow

Optical flow estimation is an essential step for many real-world compute...

DCVNet: Dilated Cost Volume Networks for Fast Optical Flow

The cost volume, capturing the similarity of possible correspondences ac...

ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching

Dense pixel matching is required for many computer vision algorithms suc...

FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network

Significant progress has been made for estimating optical flow using dee...

Optical Flow Estimation using a Spatial Pyramid Network

We learn to compute optical flow by combining a classical spatial-pyrami...

DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction

Dense optical flow estimation is challenging when there are large displa...

Code Repositories


A lightweight deep network for fast and accurate optical flow estimation.

view repo