基于非参数不确定性模型的直接体渲染绘制
Direct Volume Rendering with Nonparametric Models of Uncertainty
摘要：
我们提出了一个非参数统计框架的量化，分析和传播的数据不确定性直接体渲染（DVR）。最先进的统计DVR框架允许在可视化不确定数据时保留地面真值函数的传递函数（TF）;然而，现有的框架仅限于不确定性的参数模型。在本文中，我们解决了现有的DVR框架的局限性，通过扩展DVR框架的非参数分布。我们利用分位数插值技术来获得概率分布表示不确定性的观察光线样本强度在封闭的形式，这使得准确和有效的计算。我们评估我们提出的非参数统计模型，通过定性和定量的平均场和参数统计模型，如均匀和高斯，以及高斯混合物的比较。此外，我们提出了一个扩展的国家的最先进的渲染参数框架，以2D TF的DVR分类改进。我们展示了我们的不确定性量化框架对标量场数据集的集合、下采样和双变量版本的适用性。
英文摘要：
We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In this paper, we address the limitations of the existing DVR framework by extending the DVR framework for nonparametric distributions. We exploit the quantile interpolation technique to derive probability distributions representing uncertainty in viewing-ray sample intensities in closed form, which allows for accurate and efficient computation. We evaluate our proposed nonparametric statistical models through qualitative and quantitative comparisons with the mean-field and parametric statistical models, such as uniform and Gaussian, as well as Gaussian mixtures. In addition, we present an extension of the state-of-the-art rendering parametric framework to 2D TFs for improved DVR classifications. We show the applicability of our uncertainty quantification framework to ensemble, downsampled, and bivariate versions of scalar field datasets.
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