An interaction-aware, perceptual model for non-linear elastic objects
| Autor: | Desai Chen, Michal Piovarči, Wojciech Matusik, Hanspeter Pfister, Piotr Didyk, Jason Rebello, Roman Ďurikovič, David I. W. Levin |
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| Přispěvatelé: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Chen, Desai, Matusik, Wojciech |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject 05 social sciences 020207 software engineering Context (language use) 02 engineering and technology Space (commercial competition) Somatosensory system Object (computer science) Computer Graphics and Computer-Aided Design 050105 experimental psychology Nonlinear system Human–computer interaction Perception 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Computer vision Artificial intelligence business media_common Haptic technology |
| Zdroj: | Other univ. web domain |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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