Gradient-domain rendering accelerates realistic image synthesis by also estimating pixel color differences, which helps reconstruct high frequencies in the image domain. Converged images still require many samples per pixel even with denoising, and …
Spatiotemporal resampling methods such as ReSTIR decrease noise in Monte Carlo rendering of dynamic content by reusing paths across frames and pixels. Standard ReSTIR reuses spatially from a small number of randomly selected neighbors. This reuse …
We present ReSTIR Path Guiding (ReSTIR-PG), a real-time method that extracts guiding distributions from resampled paths produced by ReSTIR and uses them to generate improved initial candidates for the next frame. While ReSTIR significantly reduces …
Caustics rendering remains a long-standing challenge in Monte Carlo rendering because high-energy specular paths occupy only a small region of path space, making them difficult to sample effectively. Recent work such as Specular Manifold Sampling …
Recent extensions to spatiotemporal path reuse, or ReSTIR, improve rendering efficiency in the presence of high-frequency content by augmenting path reservoirs to represent contributions over full pixel footprints. Still, if historical paths fail to …
Recent spatiotemporal resampling algorithms (ReSTIR) accelerate real-time path tracing by reusing samples between pixels and frames. However, existing methods are limited by the sampling quality of path tracing, making them inefficient for scenes …
We present a practical method targeting dynamic shadow maps for many light sources in real-time rendering. We compute full-resolution shadow maps for a subset of lights, which we select with spatiotemporal reservoir resampling (ReSTIR). Our selection …
Recent advancements in spatiotemporal reservoir resampling (ReSTIR) leverage sample reuse from neighbors to efficiently evaluate the path integral. Like rasterization, ReSTIR methods implicitly assume a pinhole camera and evaluate the light arriving …
Monte Carlo rendering algorithms often utilize correlations between pixels to improve efficiency and enhance image quality. For real-time applications in particular, repeated reservoir resampling offers a powerful framework to reuse samples both …
Recent work on generalized resampled importance sampling (GRIS) enables importance-sampled Monte Carlo integration with random variable weights replacing the usual division by probability density. This enables very flexible spatiotemporal sample …