基于主成分分析的网格分解

Jung-Shiong Chang, A. C. Shih, Hsiao-Rong Tyan, Wen-Hsien Fang
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引用次数: 10

摘要

本文提出了一种基于主成分分析和布尔运算的网格自动分解技术。首先,计算光滑三维网格上每个双顶点的归一化突出度;顶点的突出度和顶点的三维坐标构成一个四维特征向量,我们用它来表示多边形网格。由于三维物体由大量多边形网格组成,我们将PCA应用于四维特征向量集。我们将前三个主成分对应的轴作为新坐标系的三个轴,并将四维向量集投影到该坐标系上。令人惊讶的是,沿第一个轴的投影数据揭示了三维物体的显著结构。因此,使用第一分量轴作为搜索基础,我们可以识别任意三维物体的所有显著部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Principal Component Analysis-based Mesh Decomposition
In this paper, we propose an automatic mesh decomposition technique based on principal component analysis (PCA) and Boolean operations. First, we calculate the normalized protrusion degree of each dual vertex on the smoothed 3-D mesh. The protrusion degree of a vertex and the vertex's 3-D coordinates form a 4-D feature vector, which we use to represent the polygon mesh. Since a 3-D object is composed of a large number of polygon meshes, we apply PCA to the set of 4-D feature vectors. We take the axes corresponding to the top three principal components as the three axes of a new coordinate system and project the set of 4-D vectors onto the system. Surprisingly, the projected data along the first axis reveals the salient structures of the 3-D object. Therefore, using the first component axis as the search basis, we can identify all the salient parts of an arbitrary 3-D object.
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