"Anisotropic density estimation for photon mapping" by Fu-Jun Luan, Li-Fan Wu et al.

Article Title

Anisotropic density estimation for photon mapping

Authors

Fu-Jun Luan, TNList, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Li-Fan Wu, TNList, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Kun Xu, TNList, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Keywords

photon mapping, density estimation, anisotropic, anisotropic spherical Gaussian

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.