Quantized Point-Based Global Illumination

Abstract

Point-based global illumination (PBGI) uses a dense point sampling of the scene's surfaces to approximate indirect light transport and is intensively used in 3D motion pictures and special effects. Each point caches the reflected light using a spherical function and is typically used in a subsequent rasterization process to compute color bleeding and ambient occlusion in an economic, noise-free fashion. The entire point set is organized in a spatial tree structure which models the light transport hierarchically, enabling fast adaptive shading on receivers (e. g., unprojected pixels). One of the major limitations of PBGI is related to the size of this tree, which can quickly become too large to fit in memory for complex scenes. However, we observe that, just as with natural images, this point data set is extremely redundant. In this paper, we present a new method exploiting this redundancy by factorizing PBGI data over the tree nodes. In particular, we show that a k-means clustering in the parameter space of the spherical functions allows to define a small number of representative nodes against which any new one can be classified. These representative functions, gathered in a pre-process over a subset of the actual points, form a look-up table which allows to substitute node's data by quantized integers in a streaming process, avoiding building the full tree before compressing it. Depending on the nodes' spherical function variance in the scene and the desired accuracy, our indexed PBGI representation achieves between one and two orders of magnitude compression of the nodes spherical functions, with negligible numerical and perceptual error in the final image. In the case of a binary tree with one surfel per leaf and no spherical functions in the leaves, this leads to compression rates ranging from 3x to 5x for the whole tree.