State-of-the-art in Multi-Light Image Collections for Surface Visualization and Analysis

Multi-Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up-to-date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.

@Article{PDCGG19,
  author       = {Pintus, R. and Dulache, T. and Ciortan, I. and Gobbetti, E. and Giachetti, A.},
  title        = {State-of-the-art in Multi-Light Image Collections for Surface Visualization and Analysis},
  journal      = {Computer Graphics Forum},
  number       = {3},
  volume       = {38},
  pages        = {909--934},
  year         = {2019},
  keywords     = {MLICs integrative view, MLIC fusion visualization, raw data direct exploration},
  doi          = {10.1111/cgf.13732},
  url          = {https://publications.crs4.it/pubdocs/2019/PDCGG19},
}