An appearance uniformity metric for 3D printing

Michael Ludwig, Gary W Meyer, Ingeborg Tastl, Nathan Moroney, Melanie Gottwals

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 reectance variations over a material’s surface. Motivated by Schrödinger’s line element approach to studying color dierences, 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 ne dierences between appearances along a path. We dene two path functions, one interpolating reectance parameters and the other interpolating the nal imagery. An image-based uniformity model is developed, applying a trained neural network to color dierences calculated from rendered images of the printed non-uniformities. The nal model is shown to perform better than commonly used image comparison algorithms, including spatial pattern classes that were not used in training.

Original languageEnglish (US)
Title of host publicationProceedings - SAP 2018
Subtitle of host publicationACM Symposium on Applied Perception
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450358941
DOIs
StatePublished - Aug 10 2018
Event15th International ACM Symposium on Applied Perception, SAP 2018 - Vancouver, Canada
Duration: Aug 10 2018Aug 11 2018

Other

Other15th International ACM Symposium on Applied Perception, SAP 2018
CountryCanada
CityVancouver
Period8/10/188/11/18

Fingerprint

Uniformity
Printing
Color
Metric
Non-uniformity
Path
Human Visual System
Line
Spatial Pattern
Roughness
Observer
Lighting
Surface roughness
Model
Neural Networks
Neural networks
Experiment
Experiments

Keywords

  • 3D printing
  • Appearance uniformity
  • Neural networks
  • Spatially-varying appearance perception

Cite this

Ludwig, M., Meyer, G. W., Tastl, I., Moroney, N., & Gottwals, M. (2018). An appearance uniformity metric for 3D printing. In S. N. Spencer (Ed.), Proceedings - SAP 2018: ACM Symposium on Applied Perception [a14] Association for Computing Machinery, Inc. https://doi.org/10.1145/3225153.3225169

An appearance uniformity metric for 3D printing. / Ludwig, Michael; Meyer, Gary W; Tastl, Ingeborg; Moroney, Nathan; Gottwals, Melanie.

Proceedings - SAP 2018: ACM Symposium on Applied Perception. ed. / Stephen N. Spencer. Association for Computing Machinery, Inc, 2018. a14.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ludwig, M, Meyer, GW, Tastl, I, Moroney, N & Gottwals, M 2018, An appearance uniformity metric for 3D printing. in SN Spencer (ed.), Proceedings - SAP 2018: ACM Symposium on Applied Perception., a14, Association for Computing Machinery, Inc, 15th International ACM Symposium on Applied Perception, SAP 2018, Vancouver, Canada, 8/10/18. https://doi.org/10.1145/3225153.3225169
Ludwig M, Meyer GW, Tastl I, Moroney N, Gottwals M. An appearance uniformity metric for 3D printing. In Spencer SN, editor, Proceedings - SAP 2018: ACM Symposium on Applied Perception. Association for Computing Machinery, Inc. 2018. a14 https://doi.org/10.1145/3225153.3225169
Ludwig, Michael ; Meyer, Gary W ; Tastl, Ingeborg ; Moroney, Nathan ; Gottwals, Melanie. / An appearance uniformity metric for 3D printing. Proceedings - SAP 2018: ACM Symposium on Applied Perception. editor / Stephen N. Spencer. Association for Computing Machinery, Inc, 2018.
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