Key Techniques Study On Network-Oriented Volume Rendering

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.