Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred
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Authors: name: Jerred Chen, name: Ronald Clark

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.