Constant-Cost Spatio-Angular Prefiltering of Glinty Appearance Using Tensor Decomposition
Hong Deng, Yang Liu, Beibei Wang, Jian Yang, Lei Ma and, Nicolas Holzschuch, Ling-Qi Yan

TL;DR
This paper introduces a tensor decomposition-based prefiltering method for efficient rendering of glinty surface appearances, achieving constant storage and performance for complex spatio-angular queries in realistic rendering scenarios.
Contribution
It formulates glinty appearance rendering as a spatio-angular range query problem and proposes a tensor decomposition approach for fast, accurate prefiltering that overcomes storage and performance challenges.
Findings
Enables constant storage and performance in glinty appearance rendering.
Supports practical applications like BRDF evaluation and global illumination.
Achieves efficient rendering of complex microstructure effects.
Abstract
The detailed glinty appearance from complex surface microstructures enhances the level of realism, but is both space- and time-consuming to render, especially when viewed from far away (large spatial coverage) and/or illuminated by area lights (large angular coverage). In this paper, we formulate the glinty appearance rendering process as a spatio-angular range query problem of the Normal Distribution Functions (NDFs), and introduce an efficient spatio-angular prefiltering solution to it. We start by exhaustively precomputing all possible NDFs with differently sized positional coverages. Then we compress the precomputed data using tensor rank decomposition, which enables accurate and fast angular range queries. With our spatio-angular prefiltering scheme, we are able to solve both the storage and performance issues at the same time, leading to efficient rendering of glinty appearance…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Advanced Vision and Imaging
