An interaction-aware, perceptual model for non-linear elastic objects
Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-worl...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Piovarči, Michal [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: ACM transactions on graphics - New York, NY [u.a.] : ACM, 1982, 35(2016), 4, Seite 1-13 |
|---|---|
| Übergeordnetes Werk: |
volume:35 ; year:2016 ; number:4 ; pages:1-13 |
| Links: |
|---|
| DOI / URN: |
10.1145/2897824.2925885 |
|---|
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OLC1979195609 |
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| 245 | 1 | 3 | |a An interaction-aware, perceptual model for non-linear elastic objects |
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| 520 | |a Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. | ||
| 650 | 4 | |a fabrication | |
| 650 | 4 | |a haptics | |
| 650 | 4 | |a compliance | |
| 650 | 4 | |a perception | |
| 700 | 1 | |a Levin, David |4 oth | |
| 700 | 1 | |a Rebello, Jason |4 oth | |
| 700 | 1 | |a Chen, Desai |4 oth | |
| 700 | 1 | |a Ďurikovič, Roman |4 oth | |
| 700 | 1 | |a Pfister, Hanspeter |4 oth | |
| 700 | 1 | |a Matusik, Wojciech |4 oth | |
| 700 | 1 | |a Didyk, Piotr |4 oth | |
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10.1145/2897824.2925885 doi PQ20160815 (DE-627)OLC1979195609 (DE-599)GBVOLC1979195609 (PRQ)a595-308ad49decf4652391bd06e145dfefbaa66b6829d7f94b94296b210bba2cd5110 (KEY)0113852920160000035000400001interactionawareperceptualmodelfornonlinearelastic DE-627 ger DE-627 rakwb eng 004 DNB Piovarči, Michal verfasserin aut An interaction-aware, perceptual model for non-linear elastic objects 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. fabrication haptics compliance perception Levin, David oth Rebello, Jason oth Chen, Desai oth Ďurikovič, Roman oth Pfister, Hanspeter oth Matusik, Wojciech oth Didyk, Piotr oth Enthalten in ACM transactions on graphics New York, NY [u.a.] : ACM, 1982 35(2016), 4, Seite 1-13 (DE-627)13041509X (DE-600)625686-7 (DE-576)015917770 0730-0301 nnns volume:35 year:2016 number:4 pages:1-13 http://dx.doi.org/10.1145/2897824.2925885 Volltext http://dl.acm.org/citation.cfm?id=2925885 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2016 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 GBV_ILN_4317 AR 35 2016 4 1-13 |
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10.1145/2897824.2925885 doi PQ20160815 (DE-627)OLC1979195609 (DE-599)GBVOLC1979195609 (PRQ)a595-308ad49decf4652391bd06e145dfefbaa66b6829d7f94b94296b210bba2cd5110 (KEY)0113852920160000035000400001interactionawareperceptualmodelfornonlinearelastic DE-627 ger DE-627 rakwb eng 004 DNB Piovarči, Michal verfasserin aut An interaction-aware, perceptual model for non-linear elastic objects 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. fabrication haptics compliance perception Levin, David oth Rebello, Jason oth Chen, Desai oth Ďurikovič, Roman oth Pfister, Hanspeter oth Matusik, Wojciech oth Didyk, Piotr oth Enthalten in ACM transactions on graphics New York, NY [u.a.] : ACM, 1982 35(2016), 4, Seite 1-13 (DE-627)13041509X (DE-600)625686-7 (DE-576)015917770 0730-0301 nnns volume:35 year:2016 number:4 pages:1-13 http://dx.doi.org/10.1145/2897824.2925885 Volltext http://dl.acm.org/citation.cfm?id=2925885 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2016 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 GBV_ILN_4317 AR 35 2016 4 1-13 |
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10.1145/2897824.2925885 doi PQ20160815 (DE-627)OLC1979195609 (DE-599)GBVOLC1979195609 (PRQ)a595-308ad49decf4652391bd06e145dfefbaa66b6829d7f94b94296b210bba2cd5110 (KEY)0113852920160000035000400001interactionawareperceptualmodelfornonlinearelastic DE-627 ger DE-627 rakwb eng 004 DNB Piovarči, Michal verfasserin aut An interaction-aware, perceptual model for non-linear elastic objects 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. fabrication haptics compliance perception Levin, David oth Rebello, Jason oth Chen, Desai oth Ďurikovič, Roman oth Pfister, Hanspeter oth Matusik, Wojciech oth Didyk, Piotr oth Enthalten in ACM transactions on graphics New York, NY [u.a.] : ACM, 1982 35(2016), 4, Seite 1-13 (DE-627)13041509X (DE-600)625686-7 (DE-576)015917770 0730-0301 nnns volume:35 year:2016 number:4 pages:1-13 http://dx.doi.org/10.1145/2897824.2925885 Volltext http://dl.acm.org/citation.cfm?id=2925885 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2016 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 GBV_ILN_4317 AR 35 2016 4 1-13 |
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10.1145/2897824.2925885 doi PQ20160815 (DE-627)OLC1979195609 (DE-599)GBVOLC1979195609 (PRQ)a595-308ad49decf4652391bd06e145dfefbaa66b6829d7f94b94296b210bba2cd5110 (KEY)0113852920160000035000400001interactionawareperceptualmodelfornonlinearelastic DE-627 ger DE-627 rakwb eng 004 DNB Piovarči, Michal verfasserin aut An interaction-aware, perceptual model for non-linear elastic objects 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. fabrication haptics compliance perception Levin, David oth Rebello, Jason oth Chen, Desai oth Ďurikovič, Roman oth Pfister, Hanspeter oth Matusik, Wojciech oth Didyk, Piotr oth Enthalten in ACM transactions on graphics New York, NY [u.a.] : ACM, 1982 35(2016), 4, Seite 1-13 (DE-627)13041509X (DE-600)625686-7 (DE-576)015917770 0730-0301 nnns volume:35 year:2016 number:4 pages:1-13 http://dx.doi.org/10.1145/2897824.2925885 Volltext http://dl.acm.org/citation.cfm?id=2925885 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2016 GBV_ILN_2021 GBV_ILN_2190 GBV_ILN_4125 GBV_ILN_4317 AR 35 2016 4 1-13 |
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An interaction-aware, perceptual model for non-linear elastic objects |
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Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. |
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Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. |
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Everyone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments. |
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An interaction-aware, perceptual model for non-linear elastic objects |
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