Multiple importance sampling (MIS) is a vital component of most rendering algorithms. MIS computes a weighted sum of samples from many different techniques to achieve generalization, that is, to handle a wide range of scene types and lighting effects. A key factor to the performance of MIS is the choice of weighting function. The go-to default - the balance heuristic - performs well in many cases, but prior work has shown that it can yield unsatisfactory results. A number of challenges cause this suboptimal performance, including low-variance techniques, sample correlation, and unknown sampling densities. Prior work has suggested improvements for some of these problems, but a general optimal solution has yet to be found. We propose a general and practical weight correction scheme: We optimize, on-the-fly, a set of correction factors that are multiplied into any baseline MIS heuristic (e.g., balance or power). We demonstrate that this approach yields consistently better equal-time performance on two rendering applications: bidirectional algorithms and resampled importance sampling for direct illumination.

Abstract We propose a technique for efficient storage and importance sampling of fluorescent spectral data. Fluorescence is fully described by a re‐radiation matrix, which for a given input wavelength indicates how much energy is re‐emitted at other wavelengths. However, such representation has a considerable memory footprint. To significantly reduce memory requirements, we propose the use of Gaussian mixture models for the representation of re‐radiation matrices. Instead of the full‐resolution matrix, we work with a set of Gaussian parameters that also allow direct importance sampling. Furthermore, if accuracy is of concern, a re‐radiation matrix can be used jointly with efficient importance sampling provided by the Gaussian mixture. In this paper, we present our pipeline for efficient storage of bispectral data and provide its extensive evaluation on a large set of bispectral measurements. We show that our method is robust and colour accurate even with its comparably minor memory requirements and that it can be seamlessly integrated into a standard Monte Carlo path tracer.

Monte Carlo rendering makes heavy use of mixture sampling and multiple importance sampling (MIS). Previous work has shown that control variates can be used to make such mixtures more efficient and more robust. However, the existing approaches failed to yield practical applications, chiefly because their underlying theory is based on the unrealistic assumption that a single mixture is optimized for a single integral. This is in stark contrast with rendering reality, where millions of integrals are computed---one per pixel---and each is infinitely recursive. We adapt and extend the theory introduced by previous work to tackle the challenges of real-world rendering applications. We achieve robust mixture sampling and (approximately) optimal MIS weighting for common applications such as light selection, BSDF sampling, and path guiding.