Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image

• URL: http://arxiv.org/abs/2503.17358v3
• Date: Tue, 01 Apr 2025 09:58:06 GMT
• Title: Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image
• Authors: Jerred Chen, Ronald Clark,
• Abstract summary: We propose a novel framework that leverages motion blur as a rich cue for motion estimation.<n>Our approach works by predicting a dense motion flow field and a monocular depth map directly from a single motion-blurred image.<n>Our method produces an IMU-like measurement that robustly captures fast and aggressive camera movements.
• Score: 14.485182089870928
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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