用单张模糊图像估算相机运动，像惯性传感器一样捕捉快速移动。
Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image
- 从模糊图像直接预测运动流场和深度图，再解线性最小二乘求速度。
- 在真实数据上达到顶尖的角速度与平移速度估计精度。
- 适合需要高速运动感知的机器人、VR/AR场景使用。
在许多机器人和虚拟现实/增强现实应用中，快速的相机运动会导致严重运动模糊，使现有姿态估计算法失效。本文提出一种新框架，将运动模糊视为丰富运动信息的线索，而非干扰因素。方法通过单张模糊图像直接预测稠密运动流场和单目深度图，并在小运动假设下通过线性最小二乘求解瞬时相机速度。本质上，该方法生成类似惯性测量单元（IMU）的输出，可鲁棒地捕捉快速且剧烈的相机运动。为训练模型，我们构建了一个大规模合成数据集，使用ScanNet++v2生成逼真运动模糊，并通过全可微分流水线在真实数据上端到端微调。在多个真实世界基准上的广泛评估表明，本方法在角速度和位移速度估计上均优于当前主流方法，如MASt3R和COLMAP。
原文摘要 · Abstract (English)
In many robotics and VR/AR applications, fast camera motions lead to 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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