Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs

Pintus, Ruggero; Ahsan, Moonisa; Marton, Fabio; Gobbetti, Enrico

Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs

Files

103-112.pdf (6.14 MB)

Date

2021

Authors

Pintus, Ruggero
Ahsan, Moonisa
Marton, Fabio
Gobbetti, Enrico

Publisher

The Eurographics Association

Abstract

We present a practical solution to create a relightable model from Multi-light Image Collections (MLICs) acquired using standard acquisition pipelines. The approach targets the difficult but very common situation in which the optical behavior of a flat, but visually and geometrically rich object, such as a painting or a bas relief, is measured using a fixed camera taking few images with a different local illumination. By exploiting information from neighboring pixels through a carefully crafted weighting and regularization scheme, we are able to efficiently infer subtle per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method is qualitatively and quantitatively evaluated on both synthetic data and real paintings in the scope of image-based relighting applications.

        @inproceedings{10.2312:gch.20211412
,
booktitle = {Eurographics Workshop on Graphics and Cultural Heritage
},
editor = {Hulusic, Vedad and Chalmers, Alan
},
title = {{Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs
}},
author = {Pintus, Ruggero and 
Ahsan, Moonisa and 
Marton, Fabio and 
Gobbetti, Enrico
},
year = {2021
},
publisher = {The Eurographics Association
},
ISSN = {2312-6124
},
ISBN = {978-3-03868-141-0
},
DOI = {10.2312/gch.20211412
}
}