☆ 4.5 Article Proceedings Paper

Single-pass stratified importance resampling

COMPUTER GRAPHICS FORUM (2022)

Perceptual Error Optimization for Monte Carlo Rendering

Vassillen Chizhov et al.

Summary: This paper proposes a perception-oriented framework to optimize rendering errors by distributing the error as visually pleasing blue noise in image space. It leverages models based on human perception to improve image fidelity and shows substantial improvements over prior state of the art.

ACM TRANSACTIONS ON GRAPHICS (2022)

添加到收藏夹

Article Computer Science, Software Engineering

Fast Volume Rendering with Spatiotemporal Reservoir Resampling

Daqi Lin et al.

Summary: In this study, a new direct illumination sampling technique is extended to multi-dimensional path space for volumetric media, resulting in efficient convergence of the volumetric path tracer. Through proper estimation of the sample's probability, the method achieves low-noise, interactive volumetric rendering.

ACM TRANSACTIONS ON GRAPHICS (2021)

添加到收藏夹

Article Computer Science, Software Engineering

ReSTIR GI: Path Resampling for Real-Time Path Tracing

Y. Ouyang et al.

Summary: The paper introduces an effective path sampling algorithm for indirect lighting suitable for highly parallel GPU architectures, utilizing screen-space spatio-temporal resampling principles similar to ReSTIR to resample multi-bounce indirect lighting paths obtained from path tracing. By sharing important path information across time and pixels in the image, the resulting algorithm achieves a substantial error reduction compared to path tracing, leading to high-quality path traced global illumination at real-time frame rates on modern GPUs when combined with a denoiser.

COMPUTER GRAPHICS FORUM (2021)

添加到收藏夹

Article Computer Science, Software Engineering