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|Title:||Exploiting Neighboring Pixels Similarity for Effective SV-BRDF Reconstruction from Sparse MLICs||Authors:||Pintus, Ruggero
|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||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.
|Appears in Collections:||CRS4 publications|
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