Share Email Print
cover

Proceedings Paper

A data driven BRDF model based on Gaussian process regression
Author(s): Zhuang Tian; Dongdong Weng; Jianying Hao; Yupeng Zhang; Dandan Meng
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Data driven bidirectional reflectance distribution function (BRDF) models have been widely used in computer graphics in recent years to get highly realistic illuminating appearance. Data driven BRDF model needs many sample data under varying lighting and viewing directions and it is infeasible to deal with such massive datasets directly. This paper proposes a Gaussian process regression framework to describe the BRDF model of a desired material. Gaussian process (GP), which is derived from machine learning, builds a nonlinear regression as a linear combination of data mapped to a highdimensional space. Theoretical analysis and experimental results show that the proposed GP method provides high prediction accuracy and can be used to describe the model for the surface reflectance of a material.

Paper Details

Date Published: 31 December 2013
PDF: 10 pages
Proc. SPIE 9042, 2013 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, 904211 (31 December 2013); doi: 10.1117/12.2036467
Show Author Affiliations
Zhuang Tian, Beijing Institute of Technology (China)
Dongdong Weng, Beijing Institute of Technology (China)
Jianying Hao, Beijing Institute of Technology (China)
Yupeng Zhang, China Aeronautical Radio Electronics Research Institute (China)
Dandan Meng, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 9042:
2013 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments
Yongtian Wang; Xiaocong Yuan; Yunlong Sheng; Kimio Tatsuno, Editor(s)

© SPIE. Terms of Use
Back to Top