Modeling spatially-varying reflectance based on Kernel Nyström

Abstract

We present a new method for modeling real-world surface reflectance, described with non-parametric spatially-varying bidirectional reflectance distribution functions(SVBRDF). Our method seeks to achieve high reconstruction accuracy, compactness and editability of representation meanwhile speeding up both the SVBRDF capturing and modeling processes. For a planar surface, we 1) design a fast capturing device to acquire reflectance samples at dense surface locations; 2) propose a Laplacian-based angular interpolation scheme for a 2D slice of BRDF at a given surface location, and then a Kernel Nystrom method for SVBRDF data matrix reconstruction; 3) propose a practical algorithm to extract linear-independent basis BRDFs, and to calculate blending weights through projecting reconstructed reflectance onto these bases. Results demonstrate that our approach models real-world reflectance with both high accuracy and high visual fidelity for real-time virtual environment rendering.