Search for: All records

Stochastic geometry models have enjoyed immense success in graphics for modeling interactions of light with complex phenomena such as participating media, rough surfaces, fibers, and more. Although each of these models operates on the same principle of replacing intricate geometry by a random process and deriving the average light transport across all instances thereof, they are each tailored to one specific application and are fundamentally distinct. Each type of stochastic geometry present in the scene is firmly encapsulated in its own appearance model, with its own statistics and light transport average, and no cross-talk between different models or deterministic and stochastic geometry is possible. In this paper, we derive a theory of light transport on stochastic implicit surfaces, a geometry model capable of expressing deterministic geometry, microfacet surfaces, participating media, and an exciting new continuum in between containing aggregate appearance, non-classical media, and more. Our model naturally supports spatial correlations, missing from most existing stochastic models. Our theory paves the way for tractable rendering of scenes in which all geometry is described by the same stochastic model, while leaving ample future work for developing efficient sampling and rendering algorithms. 

In recent years, reservoir-based spatiotemporal importance resampling (ReSTIR) algorithms appeared out of nowhere to take parts of the realtime rendering community by storm, with sample reuse speeding direct lighting from millions of dynamic lights [1], diffuse multi-bounce lighting [2], participating media [3], and even complex global illumination paths [4]. Highly optimized variants (e.g. [5]) can give 100x efficiency improvement over traditional ray- and path-tracing methods; this is key to achieve 30 or 60 Hz framerates. In production engines, tracing even one ray or path per pixel may only be feasible on the highest-end systems, so maximizing image quality per sample is vital. ReSTIR builds on the math in Talbot et al.'s [6] resampled importance sampling (RIS), which previously was not widely used or taught, leaving many practitioners missing key intuitions and theoretical grounding. A firm grounding is vital, as seemingly obvious "optimizations" arising during ReSTIR engine integration can silently introduce conditional probabilities and dependencies that, left ignored, add uncontrollable bias to the results. In this course, we plan to: 1. Provide concrete motivation and intuition for why ReSTIR works, where it applies, what assumptions it makes, and the limitations of today's theory and implementations; 2. Gently develop the theory, targeting attendees with basic Monte Carlo sampling experience but without prior knowledge of resampling algorithms (e.g., Talbot et al. [6]); 3. Give explicit algorithmic samples and pseudocode, pointing out easily-encountered pitfalls when implementing ReSTIR; 4. Discuss actual game integrations, highlighting the gotchas, challenges, and corner cases we encountered along the way, and highlighting ReSTIR's practical benefits. 

We introduce a general framework for transforming biased estimators into unbiased and consistent estimators for the same quantity. We show how several existing unbiased and consistent estimation strategies in rendering are special cases of this framework, and are part of a broader debiasing principle. We provide a recipe for constructing estimators using our generalized framework and demonstrate its applicability by developing novel unbiased forms of transmittance estimation, photon mapping, and finite differences. 

Efficiently rendering direct lighting from millions of dynamic light sources using Monte Carlo integration remains a challenging problem, even for off-line rendering systems. We introduce a new algorithm—ReSTIR—that renders such lighting interactively, at high quality, and without needing to maintain complex data structures. We repeatedly resample a set of candidate light samples and apply further spatial and temporal resampling to leverage information from relevant nearby samples. We derive an unbiased Monte Carlo estimator for this approach, and show that it achieves equal-error 6×-60× faster than state-of-the-art methods. A biased estimator reduces noise further and is 35×-65× faster, at the cost of some energy loss. We implemented our approach on the GPU, rendering complex scenes containing up to 3.4 million dynamic, emissive triangles in under 50 ms per frame while tracing at most 8 rays per pixel.