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Normal vector compression of 3D mesh model based on clustering and relative indexing

机译:基于聚类和相对索引的3D网格模型法向矢量压缩

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As the transmission of 3D shape models through Internet becomes more important, the compression issue of shape models gets more critical. While the compressions of topology and geometry have been explored significantly, the same issue for normal vectors has not yet been studied as much as it deserves.Presented in this paper is an approach to compress the normal vectors of a 3D mesh model using the concept of clustering and relative indexing. The model is assumed to be manifold triangular mesh model with normal vectors associated with vertices. The proposed scheme clusters the normal vectors of given model and the representative normal vector of each cluster is referred to via a mixed use of relative as well as absolute indexing concepts. It turns out that the proposed approach achieves a significant compression ratio (less than 10% of the original VRML model files) without a serious sacrifice of the visual quality. (C) 2004 Elsevier B.V. All rights reserved.
机译:随着3D形状模型通过Internet的传输变得越来越重要,形状模型的压缩问题变得越来越重要。尽管已经对拓扑和几何压缩进行了大量研究,但对法线向量的相同问题尚未进行应有的研究。本文提出了一种使用3D网格模型压缩法线向量的方法。聚类和相对索引。假定该模型是具有与顶点关联的法向矢量的流形三角形网格模型。所提出的方案将给定模型的法线向量聚类,并且通过混合使用相对和绝对索引概念来引用每个聚类的代表性法线向量。事实证明,所提出的方法可实现显着的压缩率(不到原始VRML模型文件的10%),而不会严重牺牲视觉质量。 (C)2004 Elsevier B.V.保留所有权利。

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