title: NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots
    Using Edge Devices
summary: 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.
paper_authors._id: 65f8196099c842dd939da849
paper_authors.name: Zhiyong Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-04-17T03:47:42.122Z
paper_authors._id: 661f4653702ad39754d94ac0
paper_authors.avatarUrl: /avatars/82902d642a603ce04cbcd3507661de32.svg
paper_authors.fullname: Zhiyong Zhang
paper_authors.type: user
paper_authors.user: Study-is-happy
paper_authors._id: 65f8196099c842dd939da84a
paper_authors.name: Huaizu Jiang
paper_authors._id: 65f8196099c842dd939da84b
paper_authors.name: Hanumant Singh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T12:29:15.010Z
paper_authors._id: 624f308f915512ce8b3fccee
paper_authors.avatarUrl: /avatars/0301164b6fd6ad4247cfad62fd859367.svg
paper_authors.fullname: Hanumant Singh
paper_authors.type: user
paper_authors.user: hanumant
paper_title: NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots
    Using Edge Devices
paper_summary: 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.
paper_ai_keywords: optical flow learning-based optical flow NeuFlow global-to-local scheme global matching lightweight CNN Jetson Orin Nano RTX 2080 edge computing platforms SLAM