Modified filtered importance sampling for virtual spherical Gaussian lights

Abstract

This paper proposes a modification of the filtered importance sampling method, and improves the quality of virtual spherical Gaussian light (VSGL)-based real-time glossy indirect illumination using this modification. The original filtered importance sampling method produces large overlaps of and gaps between filtering kernels for high-frequency probability density functions (PDFs). This is because the size of the filtering kernel is determined using the PDF at the sampled center of the kernel. To reduce those overlaps and gaps, this paper determines the kernel size using the integral of the PDF within the filtering kernel. Our key insight is that these integrals are approximately constant, if kernel centers are sampled using stratified sampling. Therefore, an appropriate kernel size can be obtained by solving this integral equation. Using the proposed kernel size for filtered importance sampling-based VSGL generation, undesirable artifacts are significantly reduced with a negligibly small overhead.