Bailin Yang
Visual saliency guided textured model simplification
Yang, Bailin; Li, Frederick W.B.; Wang, Xun; Xu, Mingliang; Liang, Xiaohui; Jiang, Zhaoyi; Jiang, Yanhui
Authors
Dr Frederick Li frederick.li@durham.ac.uk
Associate Professor
Xun Wang
Mingliang Xu
Xiaohui Liang
Zhaoyi Jiang
Yanhui Jiang
Abstract
Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and let the texture maps model the most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to produce a unified saliency map for identifying visually important object features. Results show that our method offers a better object rendering quality than existing methods.
Citation
Yang, B., Li, F. W., Wang, X., Xu, M., Liang, X., Jiang, Z., & Jiang, Y. (2016). Visual saliency guided textured model simplification. Visual Computer, 32(11), 1415-1432. https://doi.org/10.1007/s00371-015-1129-4
Journal Article Type | Article |
---|---|
Acceptance Date | May 16, 2015 |
Online Publication Date | May 16, 2015 |
Publication Date | Nov 1, 2016 |
Deposit Date | Jul 6, 2016 |
Publicly Available Date | Jul 6, 2016 |
Journal | Visual Computer |
Print ISSN | 0178-2789 |
Electronic ISSN | 1432-2315 |
Publisher | Springer |
Peer Reviewed | Peer Reviewed |
Volume | 32 |
Issue | 11 |
Pages | 1415-1432 |
DOI | https://doi.org/10.1007/s00371-015-1129-4 |
Public URL | https://durham-repository.worktribe.com/output/1401173 |
Files
Accepted Journal Article
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Copyright Statement
The final publication is available at Springer via https://doi.org/10.1007/s00371-015-1129-4
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