Visualization and Analysis of MLICs


Visualization and Analysis of MLICs



Multi-Light Image Collections (MLICs) are sets of digital images of an object acquired from the same fixed viewpoint but with varying lighting conditions. They provide large amounts of visual information related to both surface shape and topology and material behaviour. This high potentiality both in terms of source of geometrical and optical information, and visualization cues for object inspection and analysis leads to the development of a variety of computational tools. Algorithms that perform 3D reconstruction, image-based relighting, edge-extraction, non-photorealistic rendering or many more other tasks are required to make use of MLIC in an efficient and effective way to represent visual content in many fields.


CRS4, in collaboration with UNIVR, has developed a MLIC processing and visualization pipeline to extract meaningful shape and color information from images captured with directional or point lights. The pipeline includes photometric and geometric calibration methods, parallel robust matte-model fitting techniques, BRDF fitters, advanced interpolation methods and web-based viewers supporting interactive relighting in multispectral settings. The methods are targeted to the characterization and visualization of (mostly flat) objects.

Innovative features

  • robust fitting methods for dealing with capture inaccuracies and outliers (e.g., shadows, hilights);
  • full end-to-end pipeline from capture to feature extraction and visualization;
  • interactive data presentation using a deferred shading approach.

Potential users

Researchers in visual computing, cultural heritage experts.

Impact sectors

Cultural Institutions - Museums.

Other resources

  1. Ruggero Pintus, Tinsae Dulecha, Alberto Jaspe Villanueva, Andrea Giachetti, Irina Ciortan, and Enrico Gobbetti. Objective and Subjective Evaluation of Virtual Relighting from Reflectance Transformation Imaging Data. In The 15th Eurographics Workshop on Graphics and Cultural Heritage, October 2018.
  2. Irina Ciortan, Ruggero Pintus, Enrico Gobbetti, and Andrea Giachetti. Aging Prediction of Cultural Heritage Samples Based on Surface Microgeometry. In The 15th Eurographics Workshop on Graphics and Cultural Heritage, October 2018.
  3. Andrea Giachetti, Irina Ciortan, Claudia Daffara, Giacomo Marchioro, Ruggero Pintus, and Enrico Gobbetti. A Novel Framework for Highlight Reflectance Transformation Imaging. Computer Vision and Image Understanding, 168: 118-131, 2018.

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