Technical Papers

Date/Time: 04 – 13 December 2020
All presentations are available in the virtual platform on-demand.

Lecturer(s):
Zehui Lin, Peking University, China
Sheng Li, Peking University, China
Xinlu Zeng, Peking University, China
Congyi Zhang, Peking University, China
Jinzhu Jia, Peking University, China
Guoping Wang, Peking University, China
Dinesh Manocha, University of Maryland College Park, University of North Carolina at Chapel Hill (UNC), United States of America

Bio:

Description: We present a novel chi-squared progressive photon mapping algorithm (CPPM) that constructs an estimator by controlling the bandwidth to obtain superior image quality. Our estimator has parametric statistical advantages over prior nonparametric methods. First, we show that when a probability density function of the photon distribution is subject to uniform distribution, the radiance estimation is theoretically unbiased. Next, the local photon distribution is evaluated via chi-squared test to determine whether the photons follow the hypothesized distribution (uniform distribution) or not. If the statistical test deems that the photon distribution inside the bandwidth permits unbiased estimation, bandwidth reduction should be suspended. Finally, we present a pipeline with a bandwidth retention and conditional reduction scheme according to the test results. This pipeline not only accumulates sufficient photons for a reliable chi-squared test but also theoretically guarantees that the estimate converges to the correct solution. We evaluate our method on various benchmarks and observe significant improvement in the running time and rendering quality in terms of mean squared error over prior progressive photon mapping methods.