Semi-automatic generation of transfer functions for direct volume rendering
Semi-automatic generation of transfer functions for direct volume rendering
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DOI：
10.1145/288126.288167
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发表时间：
1998-10
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影响因子：
--
通讯作者：
G. Kindlmann;James W. Durkin
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文献类型：
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作者：
G. Kindlmann;James W. Durkin
尽管直接体绘制是可视化体数据中复杂结构的强大工具，但控制绘制过程的参数空间的大小和复杂性使得生成信息渲染具有挑战性。特别是，传递函数的规范——从数据值到可呈现的光学属性的映射——通常是一项耗时且不直观的任务。理想情况下，被可视化的数据本身应该提出一个适当的传递函数，以显示感兴趣的特征，而不是用不重要的元素模糊它们。我们证明，这是可能的一个大类标量体积数据，即感兴趣的区域是不同材料之间的边界。可以从数据值及其沿梯度方向的一阶和二阶导数这三个量之间的关系中生成一个使边界容易可见的传递函数。我们称之为直方图体积的数据结构以位置独立、计算效率高的方式捕获了整个体积中这些数量之间的关系。我们描述了直方图体积测量量的理论重要性，其计算中的实现问题，并通过分析半自动生成传递函数的方法。最后给出了该方法在理想合成数据和真实世界数据集上的结果。
Although direct volume rendering is a powerful tool for visualizing complex structures within volume data, the size and complexity of the parameter space controlling the rendering process makes generating an informative rendering challenging. In particular, the specification of the transfer function-the mapping from data values to renderable optical properties-is frequently a time consuming and unintuitive task. Ideally, the data being visualized should itself suggest an appropriate transfer function that brings out the features of interest without obscuring them with elements of little importance. We demonstrate that this is possible for a large class of scalar volume data, namely that where the regions of interest are the boundaries between different materials. A transfer function which makes boundaries readily visible can be generated from the relationship between three quantities: the data value and its first and second directional derivatives along the gradient direction. A data structure we term the histogram volume captures the relationship between these quantities throughout the volume in a position independent, computationally efficient fashion. We describe the theoretical importance of the quantities measured by the histogram volume, the implementation issues in its calculation, and a method for semiautomatic transfer function generation through its analysis. We conclude with results of the method on both idealized synthetic data as well as real world datasets.