The bidirectional texture function (BTF) has proven a valuable model for the representation of complex spatiallyvarying material reflectance. Its image-based nature, however, makes material BTFs extremely cumbersome to acquire: in order to adequately sample high-frequency details, many thousands of images of a given material as seen and lit from different directions have to be obtained. Additionally, long exposure times are required to account for the wide dynamic range exhibited by the reflectance of many real-world materials.We propose to significantly reduce the required exposure times by using illumination patterns instead of single light sources ("multiplexed illumination"). A BTF can then be produced by solving an appropriate linear system, exploiting the linearity of the superposition of light. Where necessary, we deal with signal-dependent noise by using a simple linear model derived from an existing database of material BTFs as a prior. We demonstrate the feasibility of our method for a number of real-world materials in a camera dome scenario. Figure 1. Our acquisition setup displaying an S-matrix pattern.

No abstract

Numerous applications in computer graphics and beyond benefit from accurate models for the visual appearance of real-world materials. Data-driven models like photographically acquired bidirectional texture functions (BTFs) suffer from limited sample sizes enforced by the common assumption of far-field illumination. Several materials like leather, structured wallpapers or wood contain structural elements on scales not captured by typical BTF measurements. We propose a method extending recent research by Steinhausen et al. to extrapolate BTFs for large-scale material samples from a measured and compressed BTF for a small fraction of the material sample, guided by a set of constraints. We propose combining color constraints with surface descriptors similar to normal maps as part of the constraints guiding the extrapolation process. This helps narrowing down the search space for suitable ABRDFs per texel to a large extent. To acquire surface descriptors for nearly flat materials, we build upon the idea of photometrically estimating normals. Inspired by recent work by Pan and Skala, we obtain images of the sample in four different rotations with an off-the-shelf flatbed scanner and derive surface curvature information from these. Furthermore, we simplify the extrapolation process by using a pixel-based texture synthesis scheme, reaching computational efficiency similar to texture optimization.

No abstract

No abstract