Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Rights / License
Abstract
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to estimate all parameters by minimizing the pixel-wise reprojection error to the input video via backpropagating through a rendering pipeline that accounts for motion blur by averaging the graphics output over short time intervals. For that purpose, we also estimate the camera exposure gap time within the same optimization. To account for abrupt motion changes like bounces, we model the motion trajectory as a piece-wise polynomial, and we are able to estimate the specific time of the bounce at sub-frame accuracy. Experiments on established benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.
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Publication status
published
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Editor
Book title
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Journal / series
Volume
Pages / Article No.
15969 - 15978
Publisher
IEEE
Event
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
3D from multi-view and sensors; Low-level vision; Motion and tracking; Optimization methods; Physics-based vision and shape-from X; Pose estimation and tracking
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc