Research On High-quality Texture-based Vector Visualization

Flow visualization has been a very attractive component of scientific visualization for a long time, where usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. It is always a challenging task in scientific visualization to effectively visualize these datasets.The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture-based techniques. These techniques can provide a complete dense representation of the flow field with high spatio-temporal coherency, and ignore none of details in a flow field. They are very promising in flow visulization and should be paid more attentions to.The high quality of flow visualization is pursued by many researchers in scientific visualization. This thesis presents an algorithm of high-quality texture-based vector visualization, analyses the influential factors of its rendering quality and amendatory schemes from four aspects, namely spatial coherency, temporal coherency, particles fluidity and anti-alaising. The objective is to get a general-purpose high-quality algorithm for 2D, 2.5D and 3D vector fields, including the steady and unsteady flows. The programmable graphics hardware should be made best use of to realize real-time rendering performance.The main contents of this thesis include:1. Considering the shortcoming of traditional dense texture-based methods with unclear streamlines and low-quality textures, a scheme of streamline enhancement is presented in this thesis, by applying a 1D high-pass filter to flow textures along the orthogonal flow direction. Results show that it can remarkably enhance the intensity contrast among streamlines while have no influence on the continuity of a single streamline.2. Flow animation is achieved by iterative convolution on textures, the relationship between the streamline dittering and high-passing filtering is studied in detail, and proper filter parameters are acquired to improve animation quality.3. Considering the unobvious particles flowing in the inflow boundary regions, a scheme of boundary supplement is used to modulate the characteristic of particle flows, which can be regarded as the inflow of outside particles. Practice shows that the scheme can obviously improve the characteristic of the total flow field and make the particle flows easy to be observed.4. Some shemes are studies for anti-aliasing. To reduce the aliasing of property break in the convolved texture, some random processes are imported, such as sampling jitter and random sampling distance. To reduce the aliasing which often occurs in regions with special orientation, an orientation-based sampling scheme is designed. To reduce the texture blur in regions close to critical points of a flow field, a method based on the local orientation change is designed to detect these critical points, thus the convolution process can be controlled and the texture blur can be avoided.5. A texture-based high-quality 3D flow visualization algorithm has been presented, a streamsurface-based 1D high-pass filter is adopted to enhance the intensity contrast between 3D streamlines and improve the display quality of streamlines on streamsurfaces. In combination with the importance function, our algorithm is capable of displaying interesting or characteristic regions, thus avoid too much occlusion and clutter issues.6. A volume-based 3D flow visualization algorithm has been presented. Due to the surfaces of flow volumes aligned with the flow by definition, our algorithm is able to aquire high-quality convolved textures with clear and smooth streamlines. A scheme is adopted to accelerate the rendering speed by convolving the voxels related to the final display while neglecting the others.Based on these studies, a system of high-quality texture-based flow visualization has been developed, which supports the visualization of 2D, 2.5D and 3D vector fields, including the steady and unsteady flows. The system has been used for the visualization of CFD datasets, such as combustion in a scramjet and flows around a bionic fish.