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An appearance uniformity metric for 3D printing

Published: 10 August 2018 Publication History

Abstract

A method is presented for perceptually characterizing appearance non-uniformities that result from 3D printing. In contrast to physical measurements, the model is designed to take into account the human visual system and variations in observer conditions such as lighting, point of view, and shape. Additionally, it is capable of handling spatial reflectance variations over a material's surface. Motivated by Schrödinger's line element approach to studying color differences, an image-based psychophysical experiment that explores paths between materials in appearance space is conducted. The line element concept is extended from color to spatially-varying appearances-including color, roughness and gloss-which enables the measurement of fine differences between appearances along a path. We define two path functions, one interpolating reflectance parameters and the other interpolating the final imagery. An image-based uniformity model is developed, applying a trained neural network to color differences calculated from rendered images of the printed non-uniformities. The final model is shown to perform better than commonly used image comparison algorithms, including spatial pattern classes that were not used in training.

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Cited By

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  • (2020)A review on machine learning in 3D printing: applications, potential, and challengesArtificial Intelligence Review10.1007/s10462-020-09876-9Online publication date: 16-Jul-2020

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cover image ACM Conferences
SAP '18: Proceedings of the 15th ACM Symposium on Applied Perception
August 2018
162 pages
ISBN:9781450358941
DOI:10.1145/3225153
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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Publication History

Published: 10 August 2018

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Author Tags

  1. 3D printing
  2. appearance uniformity
  3. neural networks
  4. spatially-varying appearance perception

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SAP '18
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SAP '18: ACM Symposium on Applied Perception 2018
August 10 - 11, 2018
British Columbia, Vancouver, Canada

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Cited By

View all
  • (2020)A review on machine learning in 3D printing: applications, potential, and challengesArtificial Intelligence Review10.1007/s10462-020-09876-9Online publication date: 16-Jul-2020

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