Wang Chaoli, Garcia Antonio, Shen Han-Wei
Department of Computer Science and Engineering, The Ohio State University, 395 Dreese Laboratories, Columbus, OH 43210, USA.
IEEE Trans Vis Comput Graph. 2007 Jan-Feb;13(1):122-34. doi: 10.1109/TVCG.2007.15.
For large volume visualization, an image-based quality metric is difficult to incorporate for level-of-detail selection and rendering without sacrificing the interactivity. This is because it is usually time-consuming to update view-dependent information as well as to adjust to transfer function changes. In this paper, we introduce an image-based level-of-detail selection algorithm for interactive visualization of large volumetric data. The design of our quality metric is based on an efficient way to evaluate the contribution of multiresolution data blocks to the final image. To ensure real-time update of the quality metric and interactive level-of-detail decisions, we propose a summary table scheme in response to runtime transfer function changes and a GPU-based solution for visibility estimation. Experimental results on large scientific and medical data sets demonstrate the effectiveness and efficiency of our algorithm.
对于大体积数据可视化而言，若不牺牲交互性，基于图像的质量度量难以用于细节层次选择和渲染。这是因为更新视图相关信息以及适应传递函数变化通常都很耗时。在本文中，我们介绍一种用于大体积数据交互式可视化的基于图像的细节层次选择算法。我们质量度量的设计基于一种有效方式，用于评估多分辨率数据块对最终图像的贡献。为确保质量度量的实时更新以及交互式细节层次决策，我们针对运行时传递函数变化提出一种摘要表方案，并提出一种基于GPU的可见性估计解决方案。在大型科学和医学数据集上的实验结果证明了我们算法的有效性和高效性。
