Sequential Monte Carlo instant radiosity

Abstract

The focus of this thesis is to accelerate the synthesis of physically accurate images using computers.Such images are generated by simulating how light ows in the scene using unbiased Monte Carlo algorithms. To date, the eciency of these algorithms has been too low for real-time rendering of error-free images. This limits the applicability of physically accurate image synthesis in interactive contexts, such as pre-visualization or video games.We focus on the well-known Instant Radiosity algorithm by Keller [1997], that approximates the indirect light eld using virtual point lights (VPLs). This approximation is unbiased and has the characteristic that the error is spread out over large areas in the image. This low-frequency noise manifests as an unwanted ickering eect in image sequences if not kept temporally coherent.Currently, the limited VPL budget imposed by running the algorithm at interactive rates results in images which may noticeably dier from the ground-truth.We introduce two new algorithms that alleviate these issues. The rst, clustered hierarchical importance sampling, reduces the overall error by increasing the VPL budget without incurring a signicant performance cost. It uses an unbiased Monte Carlo estimator to estimate the sensor response caused by all VPLs. We reduce the variance of this estimator with an ecient hierarchical importance sampling method. The second, sequential Monte Carlo Instant Radiosity, generates the VPLs using heuristic sampling and employs non-parametric density estimation to resolve their probability densities. As a result the algorithm is able to reduce the number of VPLs that move between frames, while also placing them in regions where they bring light to the image. This increases the quality of the individual frames while keeping the noise temporally coherent and less noticeable between frames.When combined, the two algorithms form a rendering system that performs favourably against traditional path tracing methods, both in terms of performance and quality. Unlike prior VPLbased methods, our system does not suer from the objectionable lack of temporal coherence in highly occluded scenes.

Incrementally baked global illumination

“…Barak et al [2013] extend the method from Ritschel et al by using Metropolis Hastings to select the VPLs from an importance buffer. Hedman et al [2016] solve temporal stability similar to Laine et al in combination with a sampling technique that selects VPLs of multiple light bounces efficiently according to their contribution to the image. VPLs managed in a Lighting Grid Hierarchy [Yuksel and Yuksel 2017] provides an offline rendering technique that efficiently avoids temporal aliasing.…”

Modified filtered importance sampling for virtual spherical Gaussian lights

“…Since our method requires a given PDF, it cannot be applied to sampling strategies without the PDF (e.g., sequential Monte Carlo instant radiosity [30]). …”