Irradiance regression for efficient final gathering in global illumination

作     者：Xuezhen HUANG Xin SUN Zhong REN Xin TONG Baining GUO Kun ZHOU 

作者机构：State Key Laboratory of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou 310058 China Microsoft Research Asia Beijing 100029 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2015年第9卷第3期

页      面：456-465页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 082803[工学-农业生物环境与能源工程] 07[理学] 08[工学] 0828[工学-农业工程] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：国家自然科学基金 

主　　题：global illumination photon mapping final gath-ering radiance estimation regression 

摘      要：Photon mapping is widely used for global illumi- nation rendering because of its high computational efficiency. But its efficiency is still limited, mainly by the intensive sam- piing required in final gathering, a process that is critical for removing low frequency artifacts of density estimation. In this paper, we propose a method to predict the final gather- ing estimation with direct density estimation, thereby achiev- ing high quality global illumination by photon mapping with high efficiency. We first sample the irradiance of a subset of shading points by both final gathering and direct radiance es- timation. Then we use the samples as a training set to predict the final gathered irradiance of other shading points through regression. Consequently, we are able to achieve about three times overall speedup compared with straightforward final gathering in global illumination computation with the same rendering quality.