Learning a human-perceived softness measure of virtual 3D objects

Lau, Manfred and Dev, Kapil and Dorsey, Julie and Rushmeier, Holly (2016) Learning a human-perceived softness measure of virtual 3D objects. In: SAP '16 Proceedings of the ACM Symposium on Applied Perception. ACM, USA, pp. 65-68. ISBN 9781450343831

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Abstract

We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. We collect crowdsourced data where humans rank their perception of the softness of vertex pairs on virtual 3D models. We then compute shape descriptors and use a learning to-rank approach to learn a softness measure mapping any vertex to a softness value. Finally, we demonstrate our framework with a variety of 3D shapes.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© Owner/Author ACM, 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SAP '16 Proceedings of the ACM Symposium on Applied Perception http://dx.doi.org/10.1145/2931002.2931019
ID Code:
80004
Deposited By:
Deposited On:
28 Jun 2016 08:10
Refereed?:
Yes
Published?:
Published
Last Modified:
08 Apr 2020 23:47