Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections
Ruggero Pintus, Moonisa Ahsan, Antonio Zorcolo, Fabio Bettio, Fabio Marton, and Enrico Gobbetti
June 2023
Abstract
We present a practical solution to create a relightable model from small 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 a limited number of 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 and visually pleasing per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method has a low memory footprint and is easily parallelizabile. We qualitatively and quantitatively evaluated it on both synthetic and real data in the scope of image-based relighting applications.
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Ruggero Pintus, Moonisa Ahsan, Antonio Zorcolo, Fabio Bettio, Fabio Marton, and Enrico Gobbetti. Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections. ACM Journal on Computing and Cultural Heritage (JOCCH), 16(2): 39:1-39:31, June 2023. DOI: 10.1145/3593428.
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Bibtex citation record
@article{Pintus:2023:ELS, author = {Ruggero Pintus and Moonisa Ahsan and Antonio Zorcolo and Fabio Bettio and Fabio Marton and Enrico Gobbetti}, title = {Exploiting Local Shape and Material Similarity for Effective {SV-BRDF} Reconstruction from Sparse Multi-Light Image Collections}, journal = {ACM Journal on Computing and Cultural Heritage (JOCCH)}, volume = {16}, number = {2}, pages = {39:1--39:31}, month = {June}, year = {2023}, abstract = { We present a practical solution to create a relightable model from small 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 a limited number of 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 and visually pleasing per-pixel analytical Bidirectional Reflectance Distribution Functions (BRDFs) representations from few per-pixel samples. The method has a low memory footprint and is easily parallelizabile. We qualitatively and quantitatively evaluated it on both synthetic and real data in the scope of image-based relighting applications. }, doi = {10.1145/3593428}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintus:2023:ELS'}, }
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