Exploiting Local Shape and Material Similarity for Effective SV-BRDF Reconstruction from Sparse Multi-Light Image Collections

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.

@Article{PAZBMG23,
  author       = {Pintus, R. and Ahsan, M. and Zorcolo, A. and Bettio, F. and Marton, F. and Gobbetti, E.},
  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)},
  number       = {2},
  volume       = {16},
  pages        = {39:1--39:31},
  month        = {june},
  year         = {2023},
  keywords     = {Multi-light Image Collections, MLICs, BRDFs},
  doi          = {10.1145/3593428},
  url          = {https://publications.crs4.it/pubdocs/2023/PAZBMG23},
}