Fabric Appearance Benchmark
Abstract
Appearance modeling is a difficult problem that still receives considerable attention from the graphics and vision communities. Though recent years have brought a growing number of high-quality material databases that have sparked new research, there is a general lack of evaluation benchmarks for performance assessment and fair comparisons between competing works. We therefore release a new dataset and pose a public challenge that will enable standardized evaluations. For this we measured 56 fabric samples with a commercial appearance scanner. We publish the resulting calibrated HDR images, along with baseline SVBRDF fits. The challenge is to recreate, under known light and view sampling, the appearance of a subset of unseen images. User submissions will be automatically evaluated and ranked by a set of standard image metrics.
This publication is part of the SVBRDF database Bonn, please also see the benchmark homepage.
Publisher's version, our EuroGraphics Fast Forward video.
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Bibtex
@INPROCEEDINGS{merzbach2020appbench,
author = {Merzbach, Sebastian and Klein, Reinhard},
title = {Fabric Appearance Benchmark},
booktitle = {Eurographics 2020 - Posters},
year = {2020},
month = may,
publisher = {The Eurographics Association},
abstract = {Appearance modeling is a difficult problem that still receives considerable attention from the
graphics and vision communities. Though recent years have brought a growing number of high-quality
material databases that have sparked new research, there is a general lack of evaluation benchmarks
for performance assessment and fair comparisons between competing works. We therefore release a new
dataset and pose a public challenge that will enable standardized evaluations. For this we measured
56 fabric samples with a commercial appearance scanner. We publish the resulting calibrated HDR
images, along with baseline SVBRDF fits. The challenge is to recreate, under known light and view
sampling, the appearance of a subset of unseen images. User submissions will be automatically
evaluated and ranked by a set of standard image metrics.},
doi = {10.2312/egp.20201035}
}