Publication
Uncalibrated, unified and unsupervised specular-aware photometric stereo
Conference Article
Conference
ICPR Workshop on Towards a Complete Analysis of People: From Face and Body to Clothes (T-CAP)
Edition
2022
Pages
7-20
Doc link
https://doi.org/10.1007/978-3-031-37660-3_1
File
Abstract
In this paper we present a variational approach to simultaneously recover the 3D reconstruction, reflectance, lighting and specularities of an object, all of them, from a set of RGB images. The approach works in an uncalibrated, unified and unsupervised manner, without assuming any prior knowledge of the shape geometry or training data to constrain the solution and under general lighting. To this end, the approach exploits a physically-aware image formation model that in combination with a perspective projection one and under spherical harmonics lighting gives a fully interpretable algorithm. Integrability is implicitly ensured as the shape is coded by a depth map rather than normal vectors. As a consequence, a wide variety of illumination conditions and complex geometries can be acquired. Our claims have been experimentally validated on challenging synthetic and real datasets, obtaining a good trade-off between accuracy and computational budget in comparison with competing approaches.
Categories
computer vision.
Author keywords
Specular-aware, Photometric Stereo, Uncalibrated, Unsupervised, Variational Optimization
Scientific reference
P. Estevez and A. Agudo. Uncalibrated, unified and unsupervised specular-aware photometric stereo, 2022 ICPR Workshop on Towards a Complete Analysis of People: From Face and Body to Clothes, 2022, Montreal (Canada), pp. 7-20.
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