Adaptive Fused Visualization for Large-scale Blood Flow Dataset with Particle-based Rendering
Adaptive Fused Visualization for Large-scale Blood Flow Dataset with Particle-based Rendering
复制标题
基于粒子渲染的大规模血流数据集的自适应融合可视化
DOI：
10.1007/s12650-014-0260-z
复制
发表时间：
2015
影响因子：
1.7
通讯作者：
Koji Koyamada
中科院分区：
文献类型：
--
作者：
Kun Zhao;Naohisa Sakamoto;Koji Koyamada
摘要为了帮助研究人员确定红细胞、血小板和血管壁的详细位置信息，需要对血流模拟获得的大规模体积数据集进行可视化。然而，这样一个大规模的数据集是很难可视化的交互式帧速率在正常的计算机上。然而，在可视化中还需要融合不同的对象，例如红细胞体积、血小板体积和血管壁表面，这使得可视化更具挑战性。为了解决这个问题，我们提出了一种用于通过应用基于粒子的渲染（PBR）（Sakamoto等人，Comput Graph 34（1）：34-42，2010）;（Sakamoto和Koyamada Ultra维斯176-185，2012）方法来可视化这种数据的系统。该方法不需要任何可见性排序，因此，它可以处理大规模的体数据集，不同对象的融合也很容易实现。为了在保持良好图像质量的同时实现交互式帧速率分析，我们在系统中组合联合收割机两种类型的PBR：对象空间PBR（O-PBR）（Sakamoto等人，Comput Graph 34（1）：34-42，2010）和图像空间PBR（I-PBR）（Sakamoto和Koyamada Ultra维斯176-185，2012）。O-PBR方法可以在较高的绘制速度下处理大规模数据，但当视图放大时，图像质量不够。I-PBR方法可以提供高质量的渲染，但对可渲染数据的大小有限制。为了获得最佳的血流可视化性能，我们的系统自适应切换O-PBR和I-PBR的基础上的数据大小的视锥体内和计算机资源。通过这种自适应可视化方法，可以有效地对大规模血流数据集进行可视化，融合不同的对象，同时保持交互式帧速率和良好的图像质量。图形摘要
AbstractThe visualization for the large-scale volume dataset obtained from blood flow simulations needs to be performed to help researchers to confirm the detailed positional information of the red blood cells, platelets and vascular walls. However, such a large-scale dataset is difficult to be visualized with an interactive frame rate on a normal computer. Nevertheless, the fusion of different objects, such as red blood cell volumes, platelet volumes and vascular wall surfaces, is also needed in the visualization, which makes it even more challenging. To solve this problem, we propose a system for visualizing such data by applying the particle-based rendering (PBR) (Sakamoto et al. Comput Graph 34(1):34–42, 2010); (Sakamoto and Koyamada Ultra Vis 176–185, 2012) method. This rendering method does not require any visibility sorting; thus, it can handle large-scale volume dataset, and the fusion of different objects is also easy to be implemented. To achieve an interactive frame rate analysis while maintaining good image quality, we combine two types of PBR in the system: object-space PBR (O-PBR) (Sakamoto et al. Comput Graph 34(1):34–42, 2010) and image-space PBR (I-PBR) (Sakamoto and Koyamada Ultra Vis 176–185, 2012). O-PBR method can handle large-scale data at high rendering speed but the image quality is not adequate when the view is enlarged. I-PBR method can provide high-quality rendering but have a limitation for the renderable data size. To obtain a best performance for blood flow visualization, our system adaptively switches O-PBR and I-PBR based on the data size within the view frustum and the computer resources. With this proposed adaptive visualization approach, the large-scale blood flow dataset can be efficiently visualized with the fusion of different objects while maintaining an interactive frame rate and good image quality.Graphical Abstract