readme.md

svgf: spatiotemporal variance guided filtering

denoise real time rendering input corrupted by monte carlo noise.

this module follows the paper Schied, Kaplanyan, Chaitanya, Burgess, Dachsbacher, and Lefohn: Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination, HPG 2017.

the pipeline is as follows. on the input is a buffer with albedo-demodulated irradiance and the albedo comes in separately. the noisy irradiance goes through a pre-blend pass, which merges previous samples into one buffer. this is controlled by a blendweight parameter lalpha and samples are rejected based on depth and normal difference. next, the still-noisy buffer is passed through 4 iterations of edge avoiding a trous wavelets. the edges are determined as in the original paper, but we compute variance slightly differently (filter the moments, not the standard deviation). the denoised output is then undergoing albedo modulation (denoised irradiance and albedo buffers are multiplied) and combined with the same from the last frame via taa with box clamping.

parameters

• alpha the taa blend weight for the previous frame. more means more blur
• taa the taa rejection threshold for the previous frame, in units of standard deviation
• prealpha controls the exponentially weighted average of the pre-blend pass on the noisy input samples

connectors

• mv the motion vectors to align the previous frame with the current
• input the noisy input, assumed to be irradiance
• albedo the input albedo, to be multiplied to the irradiance for final beauty
• output the final beauty output
• gbufp the previous gbuffer (normal, depth, 1st moment, 2nd moment)
• gbufc the current gbuffer (normal, depth, 1st moment, 2nd moment)