• DocumentCode
    3748462
  • Title

    A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms

  • Author

    Shida Beigpour;Andreas Kolb;Sven Kunz

  • Author_Institution
    Dept. of Comput. Graphics &
  • fYear
    2015
  • Firstpage
    172
  • Lastpage
    180
  • Abstract
    In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.
  • Keywords
    "Lighting","Image color analysis","Geometry","Cameras","Light sources","Complexity theory","Shape"
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision (ICCV), 2015 IEEE International Conference on
  • Electronic_ISBN
    2380-7504
  • Type

    conf

  • DOI
    10.1109/ICCV.2015.28
  • Filename
    7410385