NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices
Authors: name: Zhiyong Zhang, name: Aniket Gupta, name: Huaizu Jiang, name: Hanumant Singh

Real-time high-accuracy optical flow estimation is crucial for various
real-world applications. While recent learning-based optical flow methods have
achieved high accuracy, they often come with significant computational costs.
In this paper, we propose a highly efficient optical flow method that balances
high accuracy with reduced computational demands. Building upon NeuFlow v1, we
introduce new components including a much more light-weight backbone and a fast
refinement module. Both these modules help in keeping the computational demands
light while providing close to state of the art accuracy. Compares to other
state of the art methods, our model achieves a 10x-70x speedup while
maintaining comparable performance on both synthetic and real-world data. It is
capable of running at over 20 FPS on 512x384 resolution images on a Jetson Orin
Nano. The full training and evaluation code is available at
https://github.com/neufieldrobotics/NeuFlow_v2.