Stochastic layered alpha blending

Researchers have long sought efficient techniques for order-independent transparency (OIT) in a rasterization pipeline, to avoid sorting geometry prior to render. Techniques like A-buffers, k-buffers, stochastic transparency, hybrid transparency, adaptive transparency, and multi-layer alpha blending all approach the problem slightly differently with different tradeoffs. These OIT algorithms have many similarities, and our investigations allowed us to construct a continuum on which they lie. During this categorization, we identified various new algorithms including stochastic layered alpha blending (SLAB), which combines stochastic transparency's consistent and (optionally) unbiased convergence with the smaller memory footprint of k-buffers. Our approach can be seen as a stratified sampling technique for stochastic transparency, generating quality better than 32 x samples per pixel for roughly the cost and memory of 8 x stochastic samples. As with stochastic transparency, we can exchange noise for added bias; our algorithm provides an explicit parameter to trade noise for bias. At one end, this parameter gives results identical to stochastic transparency. On the other end, the results are identical to k-buffering.