title: NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices
summary: 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.
paper_authors._id: 66c3f49f359d1ee9697fba8b
paper_authors.name: Zhiyong Zhang
paper_authors._id: 66c3f49f359d1ee9697fba8c
paper_authors.name: Aniket Gupta
paper_authors._id: 66c3f49f359d1ee9697fba8d
paper_authors.name: Huaizu Jiang
paper_authors._id: 66c3f49f359d1ee9697fba8e
paper_authors.name: Hanumant Singh
paper_title: NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices
paper_summary: 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.
paper_ai_keywords: optical flow estimation learning-based optical flow methods computational costs NeuFlow v1 light-weight backbone fast refinement module computational demands speedup FPS Jetson Orin Nano