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Building volumetric appearance models of fabric using micro CT imaging

Authors:

Steve Marschner,

Kavita BalaAuthors Info & Claims

Communications of the ACM, Volume 57, Issue 11

Pages 98 - 105

https://doi.org/10.1145/2670517

Published: 27 October 2014 Publication History

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Abstract

Cloth is essential to our everyday lives; consequently, visualizing and rendering cloth has been an important area of research in graphics for decades. One important aspect contributing to the rich appearance of cloth is its complex 3D structure. Volumetric algorithms that model this 3D structure can correctly simulate the interaction of light with cloth to produce highly realistic images of cloth. But creating volumetric models of cloth is difficult: writing specialized procedures for each type of material is onerous, and requires significant programmer effort and intuition. Further, the resulting models look unrealistically "perfect" because they lack visually important features like naturally occurring irregularities.

This paper proposes a new approach to acquiring volume models, based on density data from X-ray computed tomography (CT) scans and appearance data from photographs under uncontrolled illumination. To model a material, a CT scan is made, yielding a scalar density volume. This 3D data has micron resolution details about the structure of cloth but lacks all optical information. So we combine this density data with a reference photograph of the cloth sample to infer its optical properties. We show that this approach can easily produce volume appearance models with extreme detail, and at larger scales the distinctive textures and highlights of a range of very different fabrics such as satin and velvet emerge automatically---all based simply on having accurate mesoscale geometry.

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Cited By

Meng SPan RGao WYan BPeng Y(2022)Automatic recognition of woven fabric structural parameters: a reviewArtificial Intelligence Review10.1007/s10462-022-10156-x55:8(6345-6387)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10462-022-10156-x
Serrano AMeijer Svan Rijn RCoban SReissland BHermens EBatenburg Kvan Bommel M(2020)A non-invasive imaging approach for improved assessments on the construction and the condition of historical knotted-pile carpetsJournal of Cultural Heritage10.1016/j.culher.2020.09.012Online publication date: Oct-2020
https://doi.org/10.1016/j.culher.2020.09.012
Wu HChen XZhang CZhou BZhao Q(2019)Modeling yarn-level geometry from a single micro-imageFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.180069320:9(1165-1174)Online publication date: 18-Oct-2019
https://doi.org/10.1631/FITEE.1800693
Show More Cited By

Index Terms

Building volumetric appearance models of fabric using micro CT imaging
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging
  2. Computer graphics
    1. Animation

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Published In

cover image Communications of the ACM

Communications of the ACM Volume 57, Issue 11

November 2014

95 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2684442

Editor:
Moshe Y. Vardi
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2014

Published in CACM Volume 57, Issue 11

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Cited By

Meng SPan RGao WYan BPeng Y(2022)Automatic recognition of woven fabric structural parameters: a reviewArtificial Intelligence Review10.1007/s10462-022-10156-x55:8(6345-6387)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10462-022-10156-x
Serrano AMeijer Svan Rijn RCoban SReissland BHermens EBatenburg Kvan Bommel M(2020)A non-invasive imaging approach for improved assessments on the construction and the condition of historical knotted-pile carpetsJournal of Cultural Heritage10.1016/j.culher.2020.09.012Online publication date: Oct-2020
https://doi.org/10.1016/j.culher.2020.09.012
Wu HChen XZhang CZhou BZhao Q(2019)Modeling yarn-level geometry from a single micro-imageFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.180069320:9(1165-1174)Online publication date: 18-Oct-2019
https://doi.org/10.1631/FITEE.1800693
Zhao S(2018)Micro-appearance Modeling of FabricsMathematical Insights into Advanced Computer Graphics Techniques10.1007/978-981-13-2850-3_2(7-33)Online publication date: 28-Nov-2018
https://doi.org/10.1007/978-981-13-2850-3_2

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