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

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

November 2021

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 and real data in the scope of image-based relighting applications.

Reference and download information

Ruggero Pintus, Moonisa Ahsan, Fabio Marton, and Enrico Gobbetti. Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs. In The 19th Eurographics Workshop on Graphics and Cultural Heritage, November 2021. To appear.

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Bibtex citation record

@inproceedings{Pintus:2021:ENP,
    author = {Ruggero Pintus and Moonisa Ahsan and Fabio Marton and Enrico Gobbetti},
    title = {Exploiting Neighboring Pixels Similarity for Effective {SV-BRDF} Reconstruction from Sparse MLICs},
    booktitle = {The 19th Eurographics Workshop on Graphics and Cultural Heritage},
    month = {November},
    year = {2021},
    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 and real data in the scope of image-based relighting applications. },
    note = {To appear},
    url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintus:2021:ENP'},
}