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

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.

@InProceedings{PAMG21,
  author       = {Pintus, R. and Ahsan, M. and Marton, F. and Gobbetti, E.},
  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},
  note         = {Best paper award at GCH 2021},
  keywords     = {MLICs, SV-BRDF},
  doi          = {10.2312/gch.20211412},
  url          = {https://publications.crs4.it/pubdocs/2021/PAMG21},
}