Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path Traced Global Illumination

Abstract

We introduce a reconstruction algorithm that generates a tempo- rally stable sequence of images from one path-per-pixel global illumination. To handle such noisy input, we use temporal accu- mulation to increase the e ective sample count and spatiotemporal luminance variance estimates to drive a hierarchical, image-space wavelet filter [Dammertz et al.2010]. This hierarchy allows us to distinguish between noise and detail at multiple scales using local luminance variance. Physically based light transport is a long-standing goal for real- time computer graphics. While modern games use limited forms of ray tracing, physically based Monte Carlo global illumination does not meet their30 Hzminimal performance requirement. Looking ahead to fully dynamic real-time path tracing, we expect this to only be feasible using a small number of paths per pixel. As such, image reconstruction using low sample counts is key to bringing path tracing to real-time. When compared to prior interactive reconstruction lters, our work gives approximately 10×more temporally stable results, matches reference images 5-47% be er (according to SSIM), and runs in just10 ms(±15%) on modern graphics hardware at 1920×1080 resolution.