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Machine-readable universal data format for bidirectional reflectance distribution function and BiRDview—An open-source web-based application for viewing and comparing bidirectional reflectance data

  • CSIC
  • University of Alicante
  • VTT Technical Research Centre of Finland

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Web of Science)
246 Downloads (Pure)

Abstract

Modern studies of bidirectional reflectance distribution function (BRDF) and its applications using data and machine-driven science require formatting of BRDF data according to Findable, accessible, interoperable and reusable (FAIR) data principles. As a solution a FAIR universal BRDF file-format based on Java Script Object Notation (JSON) is proposed. JSON principles as well as file structure are explained and examples are given. Automatic validation of universal BRDF file format is realized with the help of JSON schema. Furthermore, the source code and accompanying documentation are presented in dedicated supporting material files. It is expected that after its wide adoption, the proposed BRDF file format will enhance collaboration between different research groups and benefit machine-driven science. The uptake is facilitated by introducing a BiRDview—a modern open-source web-based application for BRDF visualization.

Original languageEnglish
Pages (from-to)1177-1192
Number of pages16
JournalColor Research and Application
Volume47
Issue number5
Early online date11 Mar 2022
DOIs
Publication statusPublished - Oct 2022
MoE publication typeA1 Journal article-refereed

Funding

This work has been done in the frame of the projects 16NRM08 BiRD and 18SIB03 BxDiff, that have received funding from the EMPIR programme co‐financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. The work has also been supported by the Academy of Finland Flagship Programme, Photonics Research and Innovation (PREIN), decision number: 320167.

Keywords

  • appearance
  • BRDF
  • FAIR
  • machine vision
  • visualization

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