  In this manuscript, we introduce a novel technique for sampling and
integrating direct illumination in the presence of many lights. Unlike previous
work, the presented technique importance samples the product distribution of
radiance and visibility while using bounded memory footprint and very low
sampling overhead. This is achieved by learning a compact approximation of the
target distributions over both space and time, allowing to reuse and adapt the
learnt distributions both spatially, within a frame, and temporally, across
multiple frames. Finally, the technique is amenable to massive parallelization
on GPUs and suitable for both offline and real-time rendering.
