Acquiring Bidirectional Texture Functions for Large-Scale Material Samples

Abstract

Most current acquisition setups for bidirectional texture functions (BTFs) are incapable of capturing large-scale material samples. We propose a method based on controlled texture synthesis to produce BTFs of appealing visual quality for such materials. Our approach uses as input data a complete measurement of a small fraction of the sample, together with few images of the large-scale structure controlling the synthesis process. We evaluate the applicability of our approach by reconstructing sparsified ground truth data and investigate the consequences of choosing different kinds and numbers of constraint images.