Accurate direct illumination using iterative adaptive sampling

Abstract

This paper introduces a new multipass algorithm for efficiently computing direct illumination in scenes with many lights and complex occlusion. Images are first divided into 8 x 8 pixel blocks and for each point to be shaded within a block, a probability density function (PDF) is constructed over the lights and sampled to estimate illumination using a small number of shadow rays. Information from these samples is then aggregated at both the pixel and block level and used to optimize the PDFs for the next pass. Over multiple passes the PDFs and pixel estimates are updated until convergence. Using aggregation and feedback progressively improves the sampling and automatically exploits both visibility and spatial coherence. We also use novel extensions for efficient antialiasing. Our adaptive multipass approach computes accurate direct illumination eight times faster than prior approaches in tests on several complex scenes.