面向网络的体绘制关键技术研究
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摘要
三维数据可视化是近年来发展最迅速的一项技术,在医学三维重建、计算流体力学、有限元后处理、地震地质等众多领域得到了广泛应用。但是在基于网络的体绘制研究中还存在很多亟待解决的问题。本文主要就面向网络的可视化过程中涉及到的几个关键技术问题:体数据分类、体数据压缩和体绘制加速算法等进行深入研究。
其中,针对支持向量机在体数据分类中需要用户提供训练样本,无法根据数据本身特征自动选择训练样本的问题,提出了将支持向量机与无监督聚类相结合的体数据分类算法。该算法先使用无监督聚类方法对样本进行初步分类,在经用户有限修正后将分类结果作为支持向量机的训练样本,因此不需要用户根据先验知识指定训练数据,实现了基于支持向量机的自动数据分类,并得到较好的分类结果。
为了满足大规模体数据的压缩存储和基于网络的多分辨率显示要求,本文提出了基于小波的大规模体数据压缩算法,它主要利用三维小波分解后各高频系数子带内的相关性。通过对大规模体数据进行基于块的层次小波分解和构造有效的八叉树结构来保存小波重要系数位置信息,该算法不仅有较好的压缩率,而且很容易进行基于数据块的随机访问。
小波Splatting算法是目前大规模体数据压缩域绘制的理想方法,本文在深入研究小波Splatting算法的基础上,结合最新GPU技术提出了基于GPU加速的小波Splatting算法。该算法将三维体数据小波分解后的八个子体块在GPU的两次顶点渲染中完成绘制,从而减少了绘制次数。利用小波Splatting的特点,该算法采用先进行小波系数累加后进行小波脚印卷积的方式,在GPU上实现了小波Splatting的加速绘制。实验结果表明基于GPU的方法大大加速了小波Splatting算法的绘制速度。
最后,针对大规模数据存储、网络传输和高质量快速绘制等问题,给出了一个面向网络传输的大规模数据体绘制系统框架。它充分结合两种小波域体绘制技术的优点,集成了大规模数据的压缩存储与传输、大规模数据的粗略快速绘制和局部感兴趣区域的精细高质量绘制等功能,因此它非常适合于大规模数据的远程可视化。
3D visualization of data, as one of the fastest growing technologies, has been widely used in 3D reconstruction of medicine, computational fluid dynamics, finite element post-processing, earthquake geology and other fields in recent years. However, there are still some issues need to be studied further in network-oriented visualization. Several key techniques including volume data classification, volume data compression and volume rendering acceleration are studied in this paper.
Support vector machine requires users to provide training samples in volume data classification. Volume data classification algorithm combined support vector machine and unsupervised clustering is presented to resolve the problem that support vector machine can not automatically select training samples according to data characteristics. The training data are initially classified using unsupervised clustering, and the results revised by user are used in the process of support vector machine training. Automatic volume data classification algorithm based on support vector machine performs better results without user providing training data in accordance with prior knowledge.
Wavelet based large-scale volume data compression algorithm, which mainly utilizes intra-band correlation of 3D wavelet high-frequency coefficients, is proposed to meet large-scale data storage and network-based multi-resolution rendering. Block-based wavelet transform is applied to large-scale volume data, and effective octree structure is constructed to store significant wavelet coefficients map. The algorithm is not only a better compression ratio but it’s easy to random access block-based data.
Wavelet splatting is an ideal approach of large-scale data compression domain rendering. GPU-based accelerated wavelet splatting is presented on the basis of the latest GPU technology. Eight sub-block wavelet coefficients of 3D data wavelet decomposition are accumulated by applying GPU vertex shader twice before the wavelet footprints convolution in order to reduce the number of rendering. The results show that the GPU-based method greatly speeds up the wavelet splatting.
Finally, a network-oriented large-scale data volume rendering system framework is introduced to resolve large-scale data storage, network transmission and high-quality fast rendering. It fully takes an advantage of two kind of wavelet-based volume rendering and integrates large-scale compressed data storage and transmission, rapid coarse volume rendering and high-quality rendering of local region of interest. So, it is very suitable for remote visualization of large scale volume data.
其中,针对支持向量机在体数据分类中需要用户提供训练样本,无法根据数据本身特征自动选择训练样本的问题,提出了将支持向量机与无监督聚类相结合的体数据分类算法。该算法先使用无监督聚类方法对样本进行初步分类,在经用户有限修正后将分类结果作为支持向量机的训练样本,因此不需要用户根据先验知识指定训练数据,实现了基于支持向量机的自动数据分类,并得到较好的分类结果。
为了满足大规模体数据的压缩存储和基于网络的多分辨率显示要求,本文提出了基于小波的大规模体数据压缩算法,它主要利用三维小波分解后各高频系数子带内的相关性。通过对大规模体数据进行基于块的层次小波分解和构造有效的八叉树结构来保存小波重要系数位置信息,该算法不仅有较好的压缩率,而且很容易进行基于数据块的随机访问。
小波Splatting算法是目前大规模体数据压缩域绘制的理想方法,本文在深入研究小波Splatting算法的基础上,结合最新GPU技术提出了基于GPU加速的小波Splatting算法。该算法将三维体数据小波分解后的八个子体块在GPU的两次顶点渲染中完成绘制,从而减少了绘制次数。利用小波Splatting的特点,该算法采用先进行小波系数累加后进行小波脚印卷积的方式,在GPU上实现了小波Splatting的加速绘制。实验结果表明基于GPU的方法大大加速了小波Splatting算法的绘制速度。
最后,针对大规模数据存储、网络传输和高质量快速绘制等问题,给出了一个面向网络传输的大规模数据体绘制系统框架。它充分结合两种小波域体绘制技术的优点,集成了大规模数据的压缩存储与传输、大规模数据的粗略快速绘制和局部感兴趣区域的精细高质量绘制等功能,因此它非常适合于大规模数据的远程可视化。
3D visualization of data, as one of the fastest growing technologies, has been widely used in 3D reconstruction of medicine, computational fluid dynamics, finite element post-processing, earthquake geology and other fields in recent years. However, there are still some issues need to be studied further in network-oriented visualization. Several key techniques including volume data classification, volume data compression and volume rendering acceleration are studied in this paper.
Support vector machine requires users to provide training samples in volume data classification. Volume data classification algorithm combined support vector machine and unsupervised clustering is presented to resolve the problem that support vector machine can not automatically select training samples according to data characteristics. The training data are initially classified using unsupervised clustering, and the results revised by user are used in the process of support vector machine training. Automatic volume data classification algorithm based on support vector machine performs better results without user providing training data in accordance with prior knowledge.
Wavelet based large-scale volume data compression algorithm, which mainly utilizes intra-band correlation of 3D wavelet high-frequency coefficients, is proposed to meet large-scale data storage and network-based multi-resolution rendering. Block-based wavelet transform is applied to large-scale volume data, and effective octree structure is constructed to store significant wavelet coefficients map. The algorithm is not only a better compression ratio but it’s easy to random access block-based data.
Wavelet splatting is an ideal approach of large-scale data compression domain rendering. GPU-based accelerated wavelet splatting is presented on the basis of the latest GPU technology. Eight sub-block wavelet coefficients of 3D data wavelet decomposition are accumulated by applying GPU vertex shader twice before the wavelet footprints convolution in order to reduce the number of rendering. The results show that the GPU-based method greatly speeds up the wavelet splatting.
Finally, a network-oriented large-scale data volume rendering system framework is introduced to resolve large-scale data storage, network transmission and high-quality fast rendering. It fully takes an advantage of two kind of wavelet-based volume rendering and integrates large-scale compressed data storage and transmission, rapid coarse volume rendering and high-quality rendering of local region of interest. So, it is very suitable for remote visualization of large scale volume data.
引文
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