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Robust and efficient adaptive direct lighting estimation
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AI
DOI：10.1007/s00371-013-0908-z.png)
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Abstract
En 中文
Hemispherical integrals are important for the estimation of direct lighting which has a major impact on the results of global illumination. This work proposes the population Monte Carlo hemispherical integral (PMC-HI) sampler to improve the efficiency of direct lighting estimation. The sampler is unbiased and derived from the population Monte Carlo framework which works on a population of samples and learns to be a better sampling function over iterations. Information found in one iteration can be used to guide subsequent iterations by distributing more samples to important sampling techniques to focus more efforts on the sampling sub-domains which have larger contributions to the hemispherical integrals. In addition, a cone sampling strategy is also proposed to enhance the success rate when complex occlusions exist. The images rendered with PMC-HI are compared against those rendered with multiple importance sampling (Veach and Guibas In: SIGGRAPH '95, pp 419-428, 1995), adaptive light sample distributions (Donikian et al. IEEE Trans Vis Comput Graph 12(3):353-364, 2006), and multidimensional hemispherical adaptive sampling (Hachisuka et al. ACM Trans Graph 27(3):33:1-33:10, 2008). Our PMC-HI sampler can improve rendering efficiency.
Keywords:
Ray-tracing
PMC
Monte Carlo
Global illumination
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