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  4. Local Non-Rigid Structure-from-Motion from Locally Diffeomorphic Mappings
 
conference paper

Local Non-Rigid Structure-from-Motion from Locally Diffeomorphic Mappings

Parashar, Shaifali  
•
Salzmann, Mathieu  
•
Fua, Pascal  
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Vision and Pattern Recognition (CVPR)

We propose a new formulation to the non-rigid structure-from-motion problem that only requires the deforming surface to meaning that its differential structure is preserved. This is a much weaker assumption than the traditional ones of isometry or conformality. We show that it is nevertheless sufficient to establish local correspondences between the surface in two different images and therefore to perform point-wise reconstruction using only up to first-order derivatives. We formulate differential constraints and solve them algebraically using the theory of resultants. We will demonstrate that our approach is more widely applicable, more stable in noisy and sparse imaging conditions and much faster than earlier ones, while delivering similar accuracy. The code is available at https//github.com/cvlab-epf1/diff-nrsfm/.

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Type
conference paper
DOI
10.1109/CVPR42600.2020.00213
Web of Science ID

WOS:000620679502032

Author(s)
Parashar, Shaifali  
Salzmann, Mathieu  
Fua, Pascal  
Date Issued

2020

Published in
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN of the book

978-1-7281-7168-5

Start page

2056

End page

2064

Subjects

Structure from Motion

•

diffeomorphism

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CVLAB  
Event nameEvent placeEvent date
Computer Vision and Pattern Recognition (CVPR)

Seattle, USA

June 16-18, 2020

Available on Infoscience
February 24, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/166472
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