EAGER: Improving the Quality and Editability of 2D and 3D Shapes via Crowdsourcing and Self-Crowdsourcing

二维曲线和三维曲面是计算机图形学的核心，但目前创建和编辑它们的方法受到用户专业知识的限制。这些任务中的许多都可以由任何人在基本级别上执行，但专家的表现很难实现。在这项探索性研究中，PI的目标是寻求基于众包的方法来超越这种限制，即一个群体超过个人的技能(通过许多个人各自贡献一种投入)，而一个人在可能被称为自我众包的情况下超越他或她自己的能力(通过贡献许多投入)(PI指出，众包的改善效果甚至适用于一个人的群体)。项目成果不仅将对计算机辅助设计的各种应用产生积极影响，还将对计算机辅助外科等领域产生积极影响，在这些领域，切割路径的质量可能具有生死攸关的重要性。有各种运动障碍的人也应该受益于在签署文件等常规任务中实现高质量体能表现的能力。为了确保他的发现得到广泛传播，PI将开源作为这项研究的一部分开发的软件。PI的方法是跨学科的，融合了人机交互、实验心理学和计算机图形学。在估计单个数字的情况下，如物品的重量或价格，人群的智慧已经被研究过；然而，对高维物理或创造性投入进行平均(或以其他方式聚集)的技术还没有研究。这项研究将涉及三个互补的重点：分而治之的方法，用于结合一群(新手)个人的少量贡献，从2D图像创建高质量的3D形状；算法，用于在称为时间通道的可重复使用的表示中保存图像和形状创建的时间记录，以提高可编辑性；以及算法，用于聚合重复的2D和3D曲线绘制(自我众包)。这项工作将为理解众包的局限性奠定基础；特别是众包2D到3D算法将测试与新手创建3D形状相关的几个长期存在的假设。

Two-dimensional curves and three-dimensional surfaces lie at the core of computer graphics, but current approaches to creating and editing them are limited by the user's expertise. Many of these tasks can be performed by anyone at a basic level, but expert performance is difficult to achieve. The PI's goal in this exploratory research is to seek ways to transcend this limitation based on crowdsourcing, in which a crowd surpasses the skill of the individual (by many individuals each contributing a single input), and an individual surpasses his or her own abilities (by contributing many inputs) in what might be termed self-crowdsourcing (the PI points out it has been shown that the improving effects of crowdsourcing apply even to a crowd of one). Project outcomes will positively impact not only the myriad applications of computer-aided design but also fields such as computer-assisted surgery, where the quality of a cutting path can take on life-or-death importance. Individuals with a variety of motor impairments should also benefit from the ability to achieve high-quality physical performance in tasks as routine as signing a document. To ensure broad dissemination of his findings, the PI will open source the software developed as part of this research.The PI's approach is interdisciplinary, fusing human-computer interaction, experimental psychology, and computer graphics. The wisdom of crowds has been studied for scenarios where a single number is estimated, such as the weight or price of an item; however, techniques for averaging (or otherwise aggregating) high-dimensional physical or creative inputs have not been studied. This research will involve three complementary thrusts: divide-and-conquer approaches for combining small contributions from a crowd of (novice) individuals to create a high-quality 3D shape from a 2D image; algorithms for preserving the temporal record of image and shape creation in a reusable representation called a time channel, to improve editability; and algorithms for aggregating repeated 2D and 3D curve drawings (self-crowdsourcing). The work will lay the foundation for an understanding of the limits of crowdsourcing; in particular, the crowdsourced 2D-to-3D algorithm will test several long-standing hypotheses relating to novice creation of 3D shapes.