Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
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Author / Producer
Date
2021
Publication Type
Conference Paper
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yes
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Abstract
We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image. While previous approaches address the deblurring problem only in the 2D image domain, our proposed rigorous modeling of all object properties in the 3D domain enables the correct description of arbitrary object motion. This leads to significantly better image decomposition and sharper deblurring results. We model the observed appearance of a motion-blurred object as a combination of the background and a 3D object with constant translation and rotation. Our method minimizes a loss on reconstructing the input image via differentiable rendering with suitable regularizers. This enables estimating the textured 3D mesh of the blurred object with high fidelity. Our method substantially outperforms competing approaches on several benchmarks for fast moving objects deblurring. Qualitative results show that the reconstructed 3D mesh generates high-quality temporal super-resolution and novel views of the deblurred object.
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Publication status
published
Book title
Advances in Neural Information Processing Systems 34
Journal / series
Volume
Pages / Article No.
29972 - 29983
Publisher
Curran
Event
35th Annual Conference on Neural Information Processing Systems (NeurIPS 2021)
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Methods
Software
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Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc
Notes
Poster presentation on December 8. 2021.