›› 2016, Vol. 31 ›› Issue (1): 217-224.doi: 10.1007/s11390-016-1622-x

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Regular Paper • Previous Articles    

Adaptive Photon Mapping Based on Gradient

Chun-Meng Kang(康春萌), Lu Wang*(王璐), Yan-Ning Xu(徐延宁), Xiang-Xu Meng(孟祥旭), and Yuan-Jie Song(宋元杰), Member, CCF   

  1. 1 School of Computer Science and Technology, Shandong University, Jinan 252000, China;
    2 Engineering Research Center of Digital Media Technology, Ministry of Education, Jinan 252000, China
  • Received:2014-04-16 Revised:2015-06-08 Online:2016-01-05 Published:2016-01-05
  • Contact: Lu Wang E-mail:luwang_hcivr@sdu.edu.cn
  • About author:Chun-Meng Kang is a Ph.D. candidate in School of Computer Science and Technology, Shandong University, Jinan. She received her Bachelor's degree in software engineering from Shandong University in 2011. Her research interests include photorealistic rendering and high performance computing.
  • Supported by:

    This work was partly supported by the National Natural Science Foundation of China under Grant Nos. 61472224 and 61472225, the National High Technology Research and Development 863 Program of China under Grant No. 2012AA01A306, the Special Funding of Independent Innovation and Transformation of Achievements in Shandong Province of China under Grant No. 2014ZZCX08201, Shandong Key Research and Development Program under Grant No. 2015GGX106006, Young Scholars Program of Shandong University under Grant No. 2015WLJH41, and the Special Funds of Taishan Scholar Construction Project.

Photon mapping can simulate some special effects efficiently such as shadows and caustics. Photon mapping runs in two phases:the photon map generating phase and the radiance estimation phase. In this paper, we focus on the bandwidth selection process in the second phase, as it can affect the final quality significantly. Poor results with noise arise if few photons are collected, while bias appears if a large number of photons are collected. In order to solve this issue, we propose an adaptive radiance estimation solution to obtain trade-offs between noise and bias by changing the number of neighboring photons and the shape of the collected area according to the radiance gradient. Our approach can be applied in both the direct and the indirect illumination computation. Finally, experimental results show that our approach can produce smoother quality while keeping the high frequency features perfectly compared with the original photon mapping algorithm.

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