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research-article

A similarity measure for material appearance

Authors:

Manuel Lagunas,

Sandra Malpica,

Diego Gutierrez,

Belen MasiaAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 38, Issue 4

Article No.: 135, Pages 1 - 12

https://doi.org/10.1145/3306346.3323036

Published: 12 July 2019 Publication History

Abstract

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.

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Code for the paper "A Similarity Measure for Material Appearance" presented in SIGGRAPH 2019 and published in ACM Transactions on Graphics (TOG).

The code is also available via GitHub: https://github.com/mlagunas/material-appearance-similarity

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Cited By

Christoff NTonchev K(2024)Material Attribute Estimation as Part of Telecommunication Augmented Reality, Virtual Reality, and Mixed Reality System: Systematic ReviewElectronics10.3390/electronics1313247313:13(2473)Online publication date: 25-Jun-2024
https://doi.org/10.3390/electronics13132473
Berns SVolz VTokarchuk LSnodgrass SGuckelsberger C(2024)Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video GamesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642077(1-23)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642077
Guerrero‐Viu JSubias JSerrano AStorrs KFleming RMasia BGutierrez D(2024)Predicting Perceived Gloss: Do Weak Labels Suffice?Computer Graphics Forum10.1111/cgf.1503743:2Online publication date: 27-Apr-2024
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Index Terms

A similarity measure for material appearance
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
  2. Computer graphics
    1. Graphics systems and interfaces
      1. Perception

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 38, Issue 4

August 2019

1480 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3306346

Editor:
Olga Sorkine-Hornung
ETH Zurich

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Copyright © 2019 ACM.

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Publication History

Published: 12 July 2019

Published in TOG Volume 38, Issue 4

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H2020 European Research Council
Spanish Ministry of Economy and Competitiveness
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Cited By

Christoff NTonchev K(2024)Material Attribute Estimation as Part of Telecommunication Augmented Reality, Virtual Reality, and Mixed Reality System: Systematic ReviewElectronics10.3390/electronics1313247313:13(2473)Online publication date: 25-Jun-2024
https://doi.org/10.3390/electronics13132473
Berns SVolz VTokarchuk LSnodgrass SGuckelsberger C(2024)Not All the Same: Understanding and Informing Similarity Estimation in Tile-Based Video GamesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642077(1-23)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642077
Guerrero‐Viu JSubias JSerrano AStorrs KFleming RMasia BGutierrez D(2024)Predicting Perceived Gloss: Do Weak Labels Suffice?Computer Graphics Forum10.1111/cgf.1503743:2Online publication date: 27-Apr-2024
https://doi.org/10.1111/cgf.15037
Wang MZhou JFeng WJiang YSerrano A(2024)Studying the Effect of Material and Geometry on Perceptual Outdoor IlluminationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334756030:9(6468-6480)Online publication date: Sep-2024
https://doi.org/10.1109/TVCG.2023.3347560
Zocco FHaddad WCorti AMalvezzi M(2024)A Unification Between Deep-Learning Vision, Compartmental Dynamical Thermodynamics, and Robotic Manipulation for a Circular EconomyIEEE Access10.1109/ACCESS.2024.349375512(173502-173516)Online publication date: 2024
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Tsuda HKawabata H(2023)materialmodifier: An R package of photo editing effects for material perception researchBehavior Research Methods10.3758/s13428-023-02116-256:3(2657-2674)Online publication date: 10-May-2023
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Deschaintre VGuerrero-Viu JGutierrez DBoubekeur TMasia B(2023)The Visual Language of FabricsACM Transactions on Graphics10.1145/359239142:4(1-15)Online publication date: 26-Jul-2023
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Subias JLagunas M(2023)In‐the‐wild Material Appearance Editing using Perceptual AttributesComputer Graphics Forum10.1111/cgf.1476542:2(333-345)Online publication date: 23-May-2023
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Rodriguez‐Pardo CPrieto‐Martin MCasas DGarces E(2023)How Will It Drape Like? Capturing Fabric Mechanics from Depth ImagesComputer Graphics Forum10.1111/cgf.1475042:2(149-160)Online publication date: 23-May-2023
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Drehwald MEppel SLi JHao HAspuru-Guzik A(2023)One-shot recognition of any material anywhere using contrastive learning with physics-based rendering2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.02150(23467-23476)Online publication date: 1-Oct-2023
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