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Scanning physical interaction behavior of 3D objects
Abstract
We describe a system for constructing computer models of several aspects of physical interaction behavior, by scanning the response of real objects. The behaviors we can successfully scan and model include deformation response, contact textures for interaction with force-feedback, and contact sounds. The system we describe uses a highly automated robotic facility that can scan behavior models of whole objects. We provide a comprehensive view of the modeling process, including selection of model structure, measurement, estimation, and rendering at interactive rates. The results are demonstrated with two examples: a soft stuffed toy which has significant deformation behavior, and a hard clay pot which has significant contact textures and sounds. The results described here make it possible to quickly construct physical interaction models of objects for applications in games, animation, and e-commerce.
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Cited by
FoleyAutomatic: physically-based sound effects for interactive simulation and animation
van den Doel, K., Kry, P. G., & Pai, D. K. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383322 · Cited by 305
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Metadata
- Authors
- Pai, D. K., van den Doel, K., James, D. L., Lang, J., Lloyd, J. E., Richmond, J. L., & Yau, S. H.
- Venue
- ACM SIGGRAPH 2005 Courses on - SIGGRAPH '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 154
- OpenAlex
- W2125292104
- Cited by
- 193
- References
- 0
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Pai, D. K., van den Doel, K., James, D. L., Lang, J., Lloyd, J. E., Richmond, J. L., & Yau, S. H. (2005). Scanning physical interaction behavior of 3D objects. ACM SIGGRAPH 2005 Courses on - SIGGRAPH '05. https://doi.org/10.1145/1198555.1198615
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.