Worldspace ReSTIR for direct illumination
Storing precomputed reservoir values with a normal-aware hashgrid
V.T. Stefanescu (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C.J. Peters – Mentor (TU Delft - Computer Graphics and Visualisation)
Michael Weinmann – Mentor (TU Delft - Computer Graphics and Visualisation)
Elmar Eisemann – Mentor (TU Delft - Computer Graphics and Visualisation)
G. Smaragdakis – Graduation committee member (TU Delft - Cyber Security)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This thesis addresses the challenge of initial frame noise in real-time ray tracing when using ReSTIR. We propose and evaluate an approach that integrates a normal- aware hash grid for precomputed reservoir caching to improve direct illumination. The research investigates how reservoir caching enhances visual quality alongside ReSTIR and analyzes the associated trade-offs in memory usage and performance. Our con- tribution includes the implementation and analysis of this caching strategy, assessing its impact on visual fidelity across diverse scenes. Although the method significantly reduces noise and improves initial sampling convergence, it can introduce visible grid artifacts in scenes with many light overlaps. Furthermore, this approach incurs notable memory overhead and increased frame times. This work demonstrates the potential of normal-aware hash grids for ReSTIR improvements, providing a proof-of-concept algorithm for stable, high-quality initial samples.