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2021 | OriginalPaper | Chapter

Low-Rank Registration of Images Captured Under Unknown, Varying Lighting

Authors : Matthieu Pizenberg, Yvain Quéau, Abderrahim Elmoataz

Published in: Scale Space and Variational Methods in Computer Vision

Publisher: Springer International Publishing

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Abstract

Photometric stereo infers the 3D-shape of a surface from a sequence of images captured under moving lighting and a static camera. However, in real-world scenarios the viewing angle may slightly vary, due to vibrations induced by the camera shutter, or when the camera is hand-held. In this paper, we put forward a low-rank affine registration technique for images captured under unknown, varying lighting. Optimization is carried out using convex relaxation and the alternating direction method of multipliers. The proposed method is shown to significantly improve 3D-reconstruction by photometric stereo on unaligned real-world data, and an open-source implementation is made available.

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previous chapter An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation

next chapter Towards Efficient Time Stepping for Numerical Shape Correspondence

https://github.com/mpizenberg/lowrr.

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Title: Low-Rank Registration of Images Captured Under Unknown, Varying Lighting
Authors: Matthieu Pizenberg
Yvain Quéau
Abderrahim Elmoataz
Publisher: Springer International Publishing
Book: Scale Space and Variational Methods in Computer Vision
Print ISBN: 978-3-030-75548-5

Electronic ISBN: 978-3-030-75549-2

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-75549-2_13

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