Monte Carlo Path Integration (RMSE: 0.07836) Photon Density Estimation (RMSE: 0.04638) Unified Framework (RMSE: 0.01246) Figure 1: Equal-time comparison of rendered images of a bathroom scene with realistic lighting fixtures. This scene includes both glossy reflections and complex caustics due to lighting fixtures which are common in interior design. Existing light transport simulation methods including Monte Carlo path integration and photon density estimation cannot efficiently render scenes with such lighting phenomena. Our new framework for light transport simulation automatically combines Monte Carlo path integration and photon density estimation by extending the sampling space of light transport paths, and produces a significantly more accurate solution in the same rendering time. AbstractWe present a new sampling space for light transport paths that makes it possible to describe Monte Carlo path integration and photon density estimation in the same framework. A key contribution of our paper is the introduction of vertex perturbations, which extends the space of paths with loosely coupled connections. The new framework enables the computation of path probabilities in the same space under the same measure, which allows us to use multiple importance sampling to combine Monte Carlo path integration and photon density estimation. The resulting algorithm, unified path sampling, can robustly render complex combinations and glossy surfaces and caustics that are problematic for existing light transport simulation methods.

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Figure 1: Equal-time comparison of two-bounce path tracing with our approach. Images are rendered at 1080p resolution with an NVIDIA 3090 RTX GPU without denoising. (Left) Path tracing with one sample per pixel in 8.0 ms. (Middle) ReSTIR GI using spatial and temporal resampling and one sample per pixel in 8.9 ms. Mean squared error is improved by a factor of 15.1. (Right) Path traced reference image.This is a challenging scene for path tracing, as direct lighting is concentrated in small regions, making it difficult to find indirect lighting paths. ReSTIR GI is much more effective thanks to sample reuse in both space and time.

As scenes become ever more complex and real-time applications embrace ray tracing, path sampling algorithms that maximize quality at low sample counts become vital. Recent resampling algorithms building on Talbot et al.'s [2005] resampled importance sampling (RIS) reuse paths spatiotemporally to render surprisingly complex light transport with a few samples per pixel. These reservoir-based spatiotemporal importance resamplers (ReSTIR) and their underlying RIS theory make various assumptions, including sample independence. But sample reuse introduces correlation , so ReSTIR-style iterative reuse loses most convergence guarantees that RIS theoretically provides. We introduce generalized resampled importance sampling (GRIS) to extend the theory, allowing RIS on correlated samples, with unknown PDFs and taken from varied domains. This solidifies the theoretical foundation, allowing us to derive variance bounds and convergence conditions in ReSTIR-based samplers. It also guides practical algorithm design and enables advanced path reuse between pixels via complex shift mappings. We show a path-traced resampler (ReSTIR PT) running interactively on complex scenes, capturing many-bounce diffuse and specular lighting while shading just one path per pixel. With our new theoretical foundation, we can also modify the algorithm to guarantee convergence for offline renderers.

Figure 1: Escher's Room. Charted Metropolis light transport considers path sampling methods and their primary sample space coordinates as charts of the path space, allowing to easily jump between them. In particular, it does so without requiring classical invertibility of the sampling methods, making the algorithm practical even with complex materials.