  Real-time high-accuracy optical flow estimation is a crucial component in
various applications, including localization and mapping in robotics, object
tracking, and activity recognition in computer vision. While recent
learning-based optical flow methods have achieved high accuracy, they often
come with heavy computation costs. In this paper, we propose a highly efficient
optical flow architecture, called NeuFlow, that addresses both high accuracy
and computational cost concerns. The architecture follows a global-to-local
scheme. Given the features of the input images extracted at different spatial
resolutions, global matching is employed to estimate an initial optical flow on
the 1/16 resolution, capturing large displacement, which is then refined on the
1/8 resolution with lightweight CNN layers for better accuracy. We evaluate our
approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency
improvements across different computing platforms. We achieve a notable 10x-80x
speedup compared to several state-of-the-art methods, while maintaining
comparable accuracy. Our approach achieves around 30 FPS on edge computing
platforms, which represents a significant breakthrough in deploying complex
computer vision tasks such as SLAM on small robots like drones. The full
training and evaluation code is available at
https://github.com/neufieldrobotics/NeuFlow.
