Efficient SVBRDF Acquisition with Manifold Bootstrapping - Dong, Yue; Lin, Steve; Guo, Baining (Material Appearance Modeling: A Data-Coherent Approach, 2013)
This chapter presents manifold bootstrapping, a new method for data-driven modeling of real-world, spatially varying reflectance, based on the idea that reflectance data over a given material sample forms a low-dimensional manifold. Manifold bootstrapping provides a high-resolution result in both the spatial and angular domains by decomposing reflectance measurement into two lower-dimensional phases. The first acquires representatives of high angular dimension but sampled sparsely over the surface, while the second acquires keys of low angular dimension but sampled densely over the surface. At each surface position, a local linear combination of a small number of neighboring representatives is computed to match each key, yielding a spatially variant bidirectional reflectance distribution function (SVBRDF) of high resolution. A quick capture session on simple devices yields results showing sharp and anisotropic specularity and rich spatial detail.