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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - SAP 2018 |
| Subtitle of host publication | ACM Symposium on Applied Perception |
| Editors | Stephen N. Spencer |
| Publisher | Association for Computing Machinery, Inc |
| ISBN (Electronic) | 9781450358941 |
| DOIs | |
| State | Published - Aug 10 2018 |
| Event | 15th International ACM Symposium on Applied Perception, SAP 2018 - Vancouver, Canada Duration: Aug 10 2018 → Aug 11 2018 |
Other
| Other | 15th International ACM Symposium on Applied Perception, SAP 2018 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 8/10/18 → 8/11/18 |
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Keywords
- 3D printing
- Appearance uniformity
- Neural networks
- Spatially-varying appearance perception
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - An appearance uniformity metric for 3D printing
AU - Ludwig, Michael
AU - Meyer, Gary W
AU - Tastl, Ingeborg
AU - Moroney, Nathan
AU - Gottwals, Melanie
PY - 2018/8/10
Y1 - 2018/8/10
N2 - 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.
AB - 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.
KW - 3D printing
KW - Appearance uniformity
KW - Neural networks
KW - Spatially-varying appearance perception
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UR - http://www.scopus.com/inward/citedby.url?scp=85056780670&partnerID=8YFLogxK
U2 - 10.1145/3225153.3225169
DO - 10.1145/3225153.3225169
M3 - Conference contribution
BT - Proceedings - SAP 2018
A2 - Spencer, Stephen N.
PB - Association for Computing Machinery, Inc
ER -