NeuFlow v2：边缘设备上的高效光流估计

NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices

August 19, 2024

作者: Zhiyong Zhang, Aniket Gupta, Huaizu Jiang, Hanumant Singh

cs.AI

摘要

实时高精度光流估计对于各种实际应用至关重要。尽管最近基于学习的光流方法取得了很高的精度，但往往伴随着显著的计算成本。本文提出了一种高效的光流方法，平衡了高精度和降低计算需求。在NeuFlow v1的基础上，我们引入了新组件，包括更轻量级的主干网络和快速的细化模块。这两个模块有助于保持计算需求的轻量化，同时提供接近最先进精度。与其他最先进方法相比，我们的模型在合成和实际数据上实现了10倍至70倍的加速，同时保持可比的性能。在Jetson Orin Nano上，我们的模型能够以超过20 FPS的速度运行在512x384分辨率图像上。完整的训练和评估代码可在https://github.com/neufieldrobotics/NeuFlow_v2找到。

English

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.