Extrapolation of Bidirectional Texture Functions using Texture Synthesis guided by Photometric Normals
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.
Keywords: bidirectional texture function, Photometric Stereo, reflectance, texture synthesis
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Bibtex
@INPROCEEDINGS{hcs-2015-btfextrapolation,
author = {Steinhausen, Heinz Christian and Mart{\'i}n, Rodrigo and den Brok, Dennis and Hullin, Matthias B. and
Klein, Reinhard},
title = {Extrapolation of Bidirectional Texture Functions using Texture Synthesis guided by Photometric
Normals},
booktitle = {Measuring, Modeling, and Reproducing Material Appearance II (SPIE 9398)},
volume = {9398},
number = {14},
year = {2015},
month = feb,
location = {San Francisco, USA},
keywords = {bidirectional texture function, Photometric Stereo, reflectance, texture synthesis},
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.}
}