Le Muzic M, Mindek P, Sorger J, Autin L, Goodsell D, Viola I
TU Wien, Austria.
TU Wien, Austria; VRVis Research Center, Vienna, Austria.
Comput Graph Forum. 2016 Jun;35(3):161-170. doi: 10.1111/cgf.12892.
In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine-tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer-based approach for visibility specification was valuable and effective for both, scientific and educational purposes.
在科学插图和可视化中,剖视图经常被用作在密集场景中进行遮挡管理的有效技术。我们提出了一种用于创作介观生物模型剖视图的新方法。与现有的剖切算法不同,我们利用了生物模型的特定性质。这些模型由数千个实例组成,不同类型的数量相对较少。我们的方法包括两个阶段。第一步,将裁剪对象放置在场景中,创建模型的剖视图。在此过程中,一个分层的堆叠条列表会告知用户场景中每种分子类型的实例可见性分布。第二步,通过这些条对每种分子类型的可见性进行微调,此时这些条充当交互式可见性均衡器。与领域专家对我们技术的评估证实,我们基于均衡器的可见性指定方法对于科学和教育目的都是有价值且有效的。