Improving style similarity metrics of 3D shapes

Dev, Kapil and Kim, Kwang In and Villar, Nicolas and Lau, Manfred (2016) Improving style similarity metrics of 3D shapes. In: Proceedings of Graphics Interface 2016. Canadian Human-Computer Communications Society / Société canadienne du dialogue humain-machine.. ISBN 9780994786814

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Abstract

The idea of style similarity metrics has been recently developed for various media types such as 2D clip art and 3D shapes. We explore this style metric problem and improve existing style similarity metrics of 3D shapes in four novel ways. First, we consider the color and texture of 3D shapes which are important properties that have not been previously considered. Second, we explore the effect of clustering a dataset of 3D models by comparing between style metrics for individual object types and style metrics that combine clusters of object types. Third, we explore the idea of userguided learning for this problem. Fourth, we introduce an iterative approach that can learn a metric from a general set of 3D models. We demonstrate these contributions with various classes of 3D shapes and with applications such as style-based similarity search and scene composition.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
© Owner/Author, 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 Proceedings of Graphics Interface 2016 http://dx.doi.org/10.20380/GI2016.22
ID Code:
80003
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Deposited On:
28 Jun 2016 08:02
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jun 2020 23:49