Suppr超能文献

输出敏感的三维线积分卷积

Output-sensitive 3D line integral convolution.

作者信息

Falk Martin, Weiskopf Daniel

机构信息

Visualization Research Center (VISUS), Universität Stuttgart, Stuttgart, Germany.

出版信息

IEEE Trans Vis Comput Graph. 2008 Jul-Aug;14(4):820-34. doi: 10.1109/TVCG.2008.25.

Abstract

We propose an output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is largely independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to avoid the computation of unnecessary LIC points: early-ray termination and empty-space leaping techniques are used to skip the computation of the LIC integral in a lazy-evaluation approach; both ray casting and texture slicing can be used as volume-rendering techniques. The input noise is modeled in object space to allow for temporal coherence under object and camera motion. Different noise models are discussed, covering dense representations based on filtered white noise all the way to sparse representations similar to oriented LIC. Aliasing artifacts are avoided by frequency control over the 3D noise and by employing a 3D variant of MIPmapping. A range of illumination models is applied to the LIC streamlines: different codimension-2 lighting models and a novel gradient-based illumination model that relies on precomputed gradients and does not require any direct calculation of gradients after the LIC integral is evaluated. We discuss the issue of proper sampling of the LIC and volume-rendering integrals by employing a frequency-space analysis of the noise model and the precomputed gradients. Finally, we demonstrate that our visualization approach lends itself to a fast graphics processing unit (GPU) implementation that supports both steady and unsteady flow. Therefore, this 3D LIC method allows users to interactively explore 3D flow by means of high-quality, view-dependent, and adaptive LIC volume visualization. Applications to flow visualization in combination with feature extraction and focus-and-context visualization are described, a comparison to previous methods is provided, and a detailed performance analysis is included.

摘要

我们提出了一种用于三维线积分卷积(LIC)的输出敏感型可视化方法,其渲染速度在很大程度上与数据集大小无关,主要由图像平面上输出的复杂度决定。我们的视图相关可视化方法将LIC生成与LIC结果的体绘制紧密联系起来,以避免计算不必要的LIC点:采用早期光线终止和空空间跳跃技术,以延迟评估的方式跳过LIC积分的计算;光线投射和纹理切片都可用作体绘制技术。在对象空间中对输入噪声进行建模,以允许在对象和相机运动下保持时间相干性。讨论了不同的噪声模型,涵盖从基于滤波白噪声的密集表示到类似于定向LIC的稀疏表示。通过对三维噪声进行频率控制并采用三维变体的MIP映射来避免混叠伪影。一系列光照模型应用于LIC流线:不同的二维余维光照模型和一种新颖的基于梯度的光照模型,该模型依赖于预先计算的梯度,并且在评估LIC积分后不需要任何直接的梯度计算。我们通过对噪声模型和预先计算的梯度进行频率空间分析,讨论了LIC和体绘制积分的适当采样问题。最后,我们证明我们的可视化方法适合快速图形处理单元(GPU)实现,该实现支持稳定和不稳定流动。因此,这种三维LIC方法允许用户通过高质量、视图相关和自适应的LIC体可视化来交互式探索三维流动。描述了与特征提取和聚焦与上下文可视化相结合的流动可视化应用,与先前方法进行了比较,并包括了详细的性能分析。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验