Multiple light sources and reflectance property estimation based on a mixture of spherical distributions

Hara Kenji, Ko Nishino, Katsushi Ikeuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

In this paper, we propose a new method for simultaneously estimating the illumination of the scene and the reflectance property of the object from a single image. We assume that the illumination consists of multiple point sources and the shape of the object is known. Unlike previous methods, we will recover not only the direction and intensity of the light sources, but also the number of light sources and the specular reflection parameter of the object. First, we represent the illumination on the surface of a unit sphere as a finite mixture of von Mises-Fisher distributions by deriving a spherical specular reflection model. Next, we estimate this mixture and the number of distributions. Finally, using this result as initial estimates, we refine the estimates using the original specular reflection model. We can use the results to render the object under novel lighting conditions.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Pages1627-1634
Number of pages8
DOIs
Publication statusPublished - Dec 1 2005
EventProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China
Duration: Oct 17 2005Oct 20 2005

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
VolumeII

Other

OtherProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
CountryChina
CityBeijing
Period10/17/0510/20/05

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Kenji, H., Nishino, K., & Ikeuchi, K. (2005). Multiple light sources and reflectance property estimation based on a mixture of spherical distributions. In Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 (pp. 1627-1634). [1544912] (Proceedings of the IEEE International Conference on Computer Vision; Vol. II). https://doi.org/10.1109/ICCV.2005.162