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. DOI: 10.2312/gch.20211412. Best paper award at GCH 2021.
@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. }, doi = {10.2312/gch.20211412}, note = {Best paper award at GCH 2021}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintus:2021:ENP'}, }
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