  Dense optical flow estimation plays a key role in many robotic vision tasks.
In the past few years, with the advent of deep learning, we have witnessed
great progress in optical flow estimation. However, current networks often
consist of a large number of parameters and require heavy computation costs,
largely hindering its application on low power-consumption devices such as
mobile phones. In this paper, we tackle this challenge and design a lightweight
model for fast and accurate optical flow prediction. Our proposed FastFlowNet
follows the widely-used coarse-to-fine paradigm with following innovations.
First, a new head enhanced pooling pyramid (HEPP) feature extractor is employed
to intensify high-resolution pyramid features while reducing parameters.
Second, we introduce a new 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 accelerate flow estimation with marginal
drops in accuracy. Experiments on both synthetic Sintel data and real-world
KITTI datasets demonstrate the effectiveness of the proposed approach, which
needs only 1/10 computation of comparable networks to achieve on par accuracy.
In particular, FastFlowNet only contains 1.37M parameters; and can execute at
90 FPS (with a single GTX 1080Ti) or 5.7 FPS (embedded Jetson TX2 GPU) on a
pair of Sintel images of resolution 1024x436.
