Abstract
Photon mapping is a widely used technique for global illumination rendering. In the density estimation step of photon mapping, the indirect radiance at a shading point is estimated through a filtering process using nearby stored photons; an isotropic filtering kernel is usually used. However, using an isotropic kernel is not always the optimal choice, especially for cases when eye paths intersect with surfaces with anisotropic BRDFs. In this paper, we propose an anisotropic filtering kernel for density estimation to handle such anisotropic eye paths. The anisotropic filtering kernel is derived from the recently introduced anisotropic spherical Gaussian representation of BRDFs. Compared to conventional photon mapping, our method is able to reduce rendering errors with negligible additional cost when rendering scenes containing anisotropic BRDFs.
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This article is published with open access at Springerlink.com
Fu-Jun Luan is a Ph.D. student in the Computer Science Department at Cornell University. He received his B.S. degree from Tsinghua University in 2015. He works on computer graphics with a focus on physically-based rendering and material appearance modeling.
Li-Fan Wu received his B.Eng. degree from Tsinghua University in 2015. He is a Ph.D. student of University of California, San Diego. His research interests include realistic rendering and image synthesis.
Kun Xu is an associate professor in the Department of Computer Science and Technology, Tsinghua University. He received his Ph.D. degree from Tsinghua University in 2009. His research interests include realistic rendering and image/video editing.
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Luan, FJ., Wu, LF. & Xu, K. Anisotropic density estimation for photon mapping. Comp. Visual Media 1, 221–228 (2015). https://doi.org/10.1007/s41095-015-0010-8
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DOI: https://doi.org/10.1007/s41095-015-0010-8
Keywords
- photon mapping
- density estimation
- anisotropic
- anisotropic spherical Gaussian