Abstract
Monte Carlo path tracing remains the standard approach for photorealistic image synthesis, but its high variance makes efficient rendering challenging. Reservoir-based Spatiotemporal Importance Resampling (ReSTIR) has recently emerged as a promising framework for efficiently reusing samples in real-time path tracing. However, the stochastic resampling step of ReSTIR makes it incompatible with ideal sample distributions like blue-noise. In this work, we reinterpret the ReSTIR process through the lens of dithered sampling and establish a connection between reservoir resampling and ordered blue-noise sampling. Building on this perspective, we propose a blue-noise-driven spatiotemporal resampling scheme that preserves perceptually favorable high-frequency error distribution during reuse. We provide a full integration of the proposed method into a real-time path tracer. Our experiments demonstrate that integrating blue noise into ReSTIR can improve the perceptual quality of low-sample-count rendered images across a range of scenes. Quantitative evaluations show reductions in perceptual error metrics with only marginal runtime overhead, maintaining efficiency suitable for real-time rendering applications.