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| Visual salience guided feature-aware shape simplification |
| Yong-wei Miao, Fei-xia Hu, Min-yan Chen, Zhen Liu, Hua-hao Shou |
| College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; College of Science, Zhejiang University of Technology, Hangzhou 310023, China |
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Abstract In the area of 3D digital engineering and 3D digital geometry processing, shape simplification is an important task to reduce their requirement of large memory and high time complexity. By incorporating the content-aware visual salience measure of a polygonal mesh into simplification operation, a novel feature-aware shape simplification approach is presented in this paper. Owing to the robust extraction of relief heights on 3D highly detailed meshes, our visual salience measure is defined by a center-surround operator on Gaussian-weighted relief heights in a scale-dependent manner. Guided by our visual salience map, the feature-aware shape simplification algorithm can be performed by weighting the high-dimensional feature space quadric error metric of vertex pair contractions with the weight map derived from our visual salience map. The weighted quadric error metric is calculated in a six-dimensional feature space by combining the position and normal information of mesh vertices. Experimental results demonstrate that our visual salience guided shape simplification scheme can adaptively and effectively re-sample the underlying models in a feature-aware manner, which can account for the visually salient features of the complex shapes and thus yield better visual fidelity.
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Received: 18 March 2014
Published: 06 September 2014
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视觉显著性引导的特征敏感形状简化
研究目的：在三维数字化工程和数字几何处理领域，在获取高度复杂模型的大规模采样数据过程中，由于利用三维扫描设备获取的均匀采样点数据不依赖于模型内在特征，大量采样点数据通常具有许多冗余信息，使得三维模型的远程传输、隐式曲面的快速重建、数字娱乐和虚拟现实实时显示等应用中，需要庞大内存和大量时间处理大规模采样数据。基于视觉显著特性的特征敏感形状简化技术,可在保持复杂模型显著特征的前提下减少模型数据量，从而满足特定应用的需求。
\n创新要点：提出一种视觉显著性引导的特征敏感形状简化方法。将三维复杂模型的内容敏感显著性度量引入模型顶点对的迭代收缩简化。顶点对的收缩误差由显著性加权的二次误差度量来衡量。与传统模型简化方法不同，该误差度量定义在结合模型顶点位置信息和法向量信息的6维空间上。
\n重要结论：实验结果表明，得到的重采样结果能够很好地反映模型的视觉显著特征，在模型的高显著区域采样点较稠密，在低显著区域采样点较稀疏。
关键词：
视觉显著性度量,
形状简化,
内容敏感,
加权二次误差度量,
特征敏感
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