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.
motion blur camera pose estimation motion flow field monocular depth map linear least squares problem small motion assumption IMU-like measurement ScanNet++v2 fully differentiable pipeline real-world benchmarks angular velocity estimates translational velocity estimates