NeuFlow v2: Push High-Efficiency Optical Flow To the Limit
This work addresses the need for real-time, high-accuracy optical flow in robotic applications, offering a significant improvement over existing methods that struggle with efficiency and generalization.
The paper tackles the problem of balancing accuracy and computational efficiency in optical flow estimation for robotics by introducing NeuFlow-V2, which achieves similar accuracy to state-of-the-art methods while providing 10x-70x speedups and running at over 20 FPS on a Jetson Orin Nano.
Real-time high-accuracy optical flow estimation is critical for a variety of real-world robotic applications. However, current learning-based methods often struggle to balance accuracy and computational efficiency: methods that achieve high accuracy typically demand substantial processing power, while faster approaches tend to sacrifice precision. These fast approaches specifically falter in their generalization capabilities and do not perform well across diverse real-world scenarios. In this work, we revisit the limitations of the SOTA methods and present NeuFlow-V2, a novel method that offers both - high accuracy in real-world datasets coupled with low computational overhead. In particular, we introduce a novel light-weight backbone and a fast refinement module to keep computational demands tractable while delivering accurate optical flow. Experimental results on synthetic and real-world datasets demonstrate that NeuFlow-V2 provides similar accuracy to SOTA methods while achieving 10x-70x speedups. 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.