Weigle Chris, Banks David C
UT/ORNL Joint Institiute for Computational Sciences, Department of Electrical Engineering and Computer Science, University of Tennessee, Tennessee, USA.
IEEE Trans Vis Comput Graph. 2008 Nov-Dec;14(6):1723-30. doi: 10.1109/TVCG.2008.108.
Large datasets typically contain coarse features comprised of finer sub-features. Even if the shapes of the small structures are evident in a 3D display, the aggregate shapes they suggest may not be easily inferred. From previous studies in shape perception, the evidence has not been clear whether physically-based illumination confers any advantage over local illumination for understanding scenes that arise in visualization of large data sets that contain features at two distinct scales. In this paper we show that physically-based illumination can improve the perception for some static scenes of complex 3D geometry from flow fields. We perform human-subjects experiments to quantify the effect of physically-based illumination on participant performance for two tasks: selecting the closer of two streamtubes from a field of tubes, and identifying the shape of the domain of a flow field over different densities of tubes. We find that physically-based illumination influences participant performance as strongly as perspective projection, suggesting that physically-based illumination is indeed a strong cue to the layout of complex scenes. We also find that increasing the density of tubes for the shape identification task improved participant performance under physically-based illumination but not under the traditional hardware-accelerated illumination model.
大型数据集通常包含由更精细子特征组成的粗糙特征。即使小结构的形状在三维显示中很明显，它们所暗示的总体形状也可能不容易推断出来。从以往关于形状感知的研究来看，对于理解包含两个不同尺度特征的大数据集可视化中出现的场景，基于物理的光照相对于局部光照是否具有任何优势，证据并不明确。在本文中，我们表明基于物理的光照可以改善对来自流场的复杂三维几何结构的某些静态场景的感知。我们进行了人体实验，以量化基于物理的光照对参与者在两项任务中的表现的影响：从一组流管中选择两个更靠近的流管，以及在不同密度的流管上识别流场区域的形状。我们发现基于物理的光照对参与者表现的影响与透视投影一样强烈，这表明基于物理的光照确实是复杂场景布局的一个重要线索。我们还发现，在形状识别任务中增加流管密度，在基于物理的光照下提高了参与者的表现，但在传统硬件加速光照模型下则没有。
