Back to Search Start Over

A Stable Analytical Framework for Isometric Shape-from-Template by Surface Integration

Authors :
Toby Collins
Adrien Bartoli
Daniel Pizarro
Ajad Chhatkuli
Institut Pascal (IP)
SIGMA Clermont (SIGMA Clermont)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)
Department of Electronics, University of Alcala
Universidad de Alcalá - University of Alcalá (UAH)
Université d'Edimbourg
Source :
IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2017, 39 (5), pp.833-850. ⟨10.1109/TPAMI.2016.2562622⟩, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (5), pp.833-850. ⟨10.1109/TPAMI.2016.2562622⟩
Publication Year :
2016

Abstract

Shape-from-Template (SfT) reconstructs the shape of a deforming surface from a single image, a 3D template and a deformation prior. For isometric deformations, this is a well-posed problem. However, previous methods which require no initialization break down when the perspective effects are small, which happens when the object is small or viewed from larger distances. That is, they do not handle all projection geometries. We propose stable SfT methods that accurately reconstruct the 3D shape for all projection geometries. We follow the existing approach of using first-order differential constraints and obtain local analytical solutions for depth and the first-order quantities: the depth-gradient or the surface normal. Previous methods use the depth solution directly to obtain the 3D shape. We prove that the depth solution is unstable when the projection geometry tends to affine, while the solution for the first-order quantities remain stable for all projection geometries. We therefore propose to solve SfT by first estimating the first-order quantities (either depth-gradient or surface normal) and integrating them to obtain shape. We validate our approach with extensive synthetic and real-world experiments and obtain significantly more accurate results compared to previous initialization-free methods. Our approach does not require any optimization, which makes it very fast.

Details

ISSN :
19393539 and 01628828
Volume :
39
Issue :
5
Database :
OpenAIRE
Journal :
IEEE transactions on pattern analysis and machine intelligence
Accession number :
edsair.doi.dedup.....fd339a2283fe8a8fb0d5b88a4ff00385
Full Text :
https://doi.org/10.1109/TPAMI.2016.2562622⟩