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Title: Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs
Authors: Pintus, Ruggero 
Ahsan, Moonisa 
Marton, Fabio 
Gobbetti, Enrico 
Affiliations: CRS4 
Keywords: visual computing
Issue Date: Nov-2021
Publisher: the Eurographics Association
Project: Advanced Visual and Geometric Computing for 3D Capture, Display, and Fabrication 
Related Publication(s): The 19th Eurographics Workshop on Graphics and Cultural Heritage
Start page: 103
End page: 112
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
Appears in Collections:CRS4 publications

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