拉普拉斯-贝尔特拉米算子,联合贝叶斯模型,形状检索," /> 拉普拉斯-贝尔特拉米算子,联合贝叶斯模型,形状检索,"/> 一种基于拉普拉斯算子和联合贝叶斯模型的三维形状检索方法
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Vis Inf
Vis Inf  2020, Vol. 4 Issue (3): 69-76    DOI: 10.1016/j.visinf.2020.08.002
论文     
一种基于拉普拉斯算子和联合贝叶斯模型的三维形状检索方法
Zihao Wang，Hongwei Lin <
Zhejiang University
3D shape retrieval based on Laplace operator and joint Bayesian model
Zihao Wang，Hongwei Lin
Zhejiang University
 全文: PDF 
摘要： 特征分析在计算机视觉和计算机图形学中起着重要的作用。在形状检索的任务中，形状描述符必不可少。近年来，基于深度学习的特征提取非常流行，但是由于各种形状所包含的内在信息及其可解释性，对它们的几何形状描述符的设计仍然很有意义。 本文提出了一种有效且鲁棒的三维模型描述符，基于表面拉普拉斯-贝尔特拉米算子的归一化特征函数的概率分布和用于降维的频谱方法来构造描述符，利用联合贝叶斯模型来估计描述符空间中的距离，在训练阶段引入矩阵正则化过程来重新估计协方差矩阵。最后，将3D形状检索描述符应用公共基准库。实验表明，该方法鲁棒，具有良好的检索性能。
关键词： 拉普拉斯-贝尔特拉米算子')" href="#">拉普拉斯-贝尔特拉米算子;  联合贝叶斯模型;  形状检索    
Abstract: Feature analysis plays a significant role in computer vision and computer graphics. In the task of shape retrieval, shape descriptor is indispensable. In recent years, feature extraction based on deep learning becomes very popular, but the design of geometric shape descriptor is still meaningful due to the contained intrinsic information and interpretability. This paper proposes an effective and robust descriptor of 3D models. The descriptor is constructed based on the probability distribution of the normalized eigenfunctions of the Laplace-Beltrami operator on the surface, and a spectrum method for dimensionality reduction. The distance metric of the descriptor space is learned by utilizing the joint Bayesian model, and we introduce a matrix regularization in the training stage to re-estimate the covariance matrix. Finally, we apply the descriptor to 3D shape retrieval on a public benchmark. Experiments show that our method is robust and has good retrieval performance.
Key words: Laplace-Beltrami operator    Joint Bayesian    Shape retrieval
出版日期: 2020-10-09
通讯作者: Hongwei Lin     E-mail: hwlin@zju.edu.cn
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引用本文:

Zihao Wang, Hongwei Lin. 3D shape retrieval based on Laplace operator and joint Bayesian model. Vis Inf, 2020, 4(3): 69-76.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2020.08.002    或    http://www.zjujournals.com/vi/CN/Y2020/V4/I3/69

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