title: Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred
    Image
summary: In many robotics and VR/AR applications, fast camera motions cause a high
  level of motion blur, causing existing camera pose estimation methods to fail.
  In this work, we propose a novel framework that leverages motion blur as a rich
  cue for motion estimation rather than treating it as an unwanted artifact. Our
  approach works by predicting a dense motion flow field and a monocular depth
  map directly from a single motion-blurred image. We then recover the
  instantaneous camera velocity by solving a linear least squares problem under
  the small motion assumption. In essence, our method produces an IMU-like
  measurement that robustly captures fast and aggressive camera movements. To
  train our model, we construct a large-scale dataset with realistic synthetic
  motion blur derived from ScanNet++v2 and further refine our model by training
  end-to-end on real data using our fully differentiable pipeline. Extensive
  evaluations on real-world benchmarks demonstrate that our method achieves
  state-of-the-art angular and translational velocity estimates, outperforming
  current methods like MASt3R and COLMAP.
paper_authors._id: 67e3b41d0fa8f886db6b323a
paper_authors.name: Jerred Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:13:31.357Z
paper_authors._id: 649f019c59c1ae90dbe37b8e
paper_authors.avatarUrl: /avatars/a2021fcd1342ad6d1360acdef34c9185.svg
paper_authors.fullname: Jerred Chen
paper_authors.type: user
paper_authors.user: jerredchen
paper_authors._id: 67e3b41d0fa8f886db6b323b
paper_authors.name: Ronald Clark
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:13:57.988Z
paper_authors._id: 6305ee63d70693fdf1c7dbb8
paper_authors.avatarUrl: /avatars/0e81ed3757b4e65be82063b538c3fe49.svg
paper_authors.fullname: Ronald Clark
paper_authors.type: user
paper_authors.user: r0nn13
paper_title: Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred
    Image
paper_summary: In many robotics and VR/AR applications, fast camera motions cause a high
  level of motion blur, causing existing camera pose estimation methods to fail.
  In this work, we propose a novel framework that leverages motion blur as a rich
  cue for motion estimation rather than treating it as an unwanted artifact. Our
  approach works by predicting a dense motion flow field and a monocular depth
  map directly from a single motion-blurred image. We then recover the
  instantaneous camera velocity by solving a linear least squares problem under
  the small motion assumption. In essence, our method produces an IMU-like
  measurement that robustly captures fast and aggressive camera movements. To
  train our model, we construct a large-scale dataset with realistic synthetic
  motion blur derived from ScanNet++v2 and further refine our model by training
  end-to-end on real data using our fully differentiable pipeline. Extensive
  evaluations on real-world benchmarks demonstrate that our method achieves
  state-of-the-art angular and translational velocity estimates, outperforming
  current methods like MASt3R and COLMAP.
paper_ai_summary: A novel framework estimates camera pose using motion blur as a cue, predicting motion flow and depth from a single blurry image.
paper_ai_keywords: dense motion flow field monocular depth map linear least squares problem small motion assumption IMU-like measurement ScanNet++v2 fully differentiable pipeline angular velocity estimates translational velocity estimates MASt3R COLMAP