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JACIII Vol.22 No.4 pp. 491-497
doi: 10.20965/jaciii.2018.p0491
(2018)

Paper:

Estimation of Position and Intensity of Multi-Light Sources Based on Specular Sphere

Chao Xu, Hua Li, and Cheng Han

Department of Computer Science and Technology, Changchun University of Science and Technology
Room 1603, Block B, Technology building, No.7186 Weixing Road, Changchun 130022, China

Received:
February 21, 2018
Accepted:
April 17, 2018
Published:
July 20, 2018
Keywords:
illumination estimation, multi-light sources, reference sphere
Abstract

Illumination estimation is an important research content in mixed reality technology. This paper presents a novel method for locating multiple point light sources and estimating their intensities from the images of a pair of reference spheres. In our approach, no prior knowledge of the location of the sphere is necessary, and the center of the sphere can be uniquely identified with the known radius. The sphere surface is assumed to have both Lambertian and specular properties instead of being a pure Lambertian or specular surface, which guarantees a higher accuracy than the existing approaches. The position estimations of multiple light sources are based on the fact that the specular reflection is highly dependent on highlights. One sphere is utilized to determine the directions of the light sources, and two spheres are used to locate the positions. The images of reference spheres are sampled and partitioned with multiple light sources in different positions. An illumination model is used to calculate the intensities of the ambient light and multiple light sources. Experiments on both simulation and synthetic images show that this method is feasible and accurate for estimating the positions and intensities of the multiple light sources.

Virtual-real fusion scene

Virtual-real fusion scene

Cite this article as:
C. Xu, H. Li, and C. Han, “Estimation of Position and Intensity of Multi-Light Sources Based on Specular Sphere,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.4, pp. 491-497, 2018.
Data files:
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