Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations
Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations
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DOI：
10.1109/tvcg.2019.2934260
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发表时间：
2020-01-01
影响因子：
5.2
通讯作者：
Keefe, Daniel F.
中科院分区：
文献类型：
--
作者：
Johnson, Seth;Samsel, Francesca;Keefe, Daniel F.
我们介绍了基于人工制品的渲染（ABR），这是一个工具，算法和过程框架，这些框架使得可以使用完全源自颜色，线条，纹理和形式的视觉语言来生成真实的，数据驱动的3D科学可视化，并使用使用的视觉语言来产生。传统的物理媒体或自然界中发现的。提出了ABR的理论和过程来满足三种当前需求：（i）通过使非程序员能够快速设计和批评许多替代数据与视觉映射来设计更好的可视化； （ii）扩大用于科学可视化的视觉词汇，以描绘越来越复杂的多元数据； （iii）为数据可视化带来了更具吸引力，自然和人为权利的手工制作的美学。支持ABR的新工具和算法包括用于构建基于人工制品的菌落的前端小程序，优化3D扫描的网格用于数据可视化，以及从人工制品中合成纹理。这些用自定义算法和接口的交互式渲染引擎补充，这些引擎演示了多种新的视觉样式，用于描述点，线，表面和音量数据。一项研究团队设计研究提供了可视化设计过程中变化的早期证据，与传统的科学可视化系统相比，ABR被认为可以实现。关于气候科学和大脑成像应用程序的定性用户反馈支持ABR对科学发现和公共交流的实用性。
We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.