NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
<|reference_start|>NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices: 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.<|reference_end|>
@article{zhang2024neuflow:,
    title={NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots
  Using Edge Devices},
    author={Zhiyong Zhang, Huaizu Jiang, Hanumant Singh},
    journal={arXiv preprint arXiv:2403.10425},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10425},
    primaryClass={cs.CV cs.AI cs.RO}
}