Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred
  Image
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.
67e3b41d0fa8f886db6b323a
Jerred Chen
admin_assigned
2025-03-27T10:13:31.357Z
649f019c59c1ae90dbe37b8e
/avatars/a2021fcd1342ad6d1360acdef34c9185.svg
Jerred Chen
user
jerredchen
67e3b41d0fa8f886db6b323b
Ronald Clark
admin_assigned
2025-03-27T10:13:57.988Z
6305ee63d70693fdf1c7dbb8
/avatars/0e81ed3757b4e65be82063b538c3fe49.svg
Ronald Clark
user
r0nn13
Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred
  Image
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.
A novel framework estimates camera pose using motion blur as a cue, predicting motion flow and depth from a single blurry image.
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