第六章:基于Haar小波的超大体积数据集的多分辨率表示与变形

H. Xavier, T. Sebastien
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引用次数: 5

摘要

在一个虚拟雕塑项目中,我们想实时雕刻非常大的3D物体,以体素(体素)采样。这种表示的缺点是需要大量的体素来表示非常大和详细的对象。因此,这会带来重要的内存成本和计算时间问题。为了实现实时性能,我们在本文中提出了一种结合八叉树和小波的新多分辨率模型:在八叉树中对3D对象进行粗略采样,其中每个包含数据的叶子都通过3D Haar小波变换进行薄采样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Chapter 6: Multiresolution Representation and Deformation of Very Large Volume Datasets Based on Haar Wavelets
In a virtual sculpture project, we would like to sculpt in real-time very large 3D objects sampled in volume elements (voxels). The drawback of this kind of representation is the important number of voxels required to represent very large and detailed objects. Consequently, that entails important memory cost and computation time issues. In order to allow real-time performance, we propose in this paper a new multiresolution model that combines octree and wavelet: a 3D object is roughly sampled in an octree, where each leaf containing data is thinly sampled thanks to a 3D Haar wavelet transform.
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