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水平上执行，但专家的性能很难实现。 PI在这项探索性研究中的目标是寻求基于众包的方法来超越这种限制，其中群体超越了个人的技能（由许多个人各自贡献一个输入），一个人超越了他或她自己的能力（通过提供许多投入），（PI指出，已经表明，众包的改善效果甚至适用于一个人的人群）。 项目成果不仅将对计算机辅助设计的无数应用产生积极影响，还将对计算机辅助手术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从在签署文件等例行任务中实现高质量身体表现的能力中受益。 为了确保他的研究结果得到广泛传播，PI将开放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。 群体智慧已经被研究用于估计单个数字的场景，例如物品的重量或价格;然而，用于平均（或以其他方式聚合）高维物理或创造性输入的技术尚未被研究。 这项研究将涉及三个互补的推力：分而治之的方法，结合小的贡献，从一群（新手）个人创建一个高质量的3D形状从2D图像;算法，保留图像和形状创建的时间记录在一个可重用的表示称为时间通道，以提高可编辑性;以及用于聚合重复的2D和3D曲线绘图的算法（自众包）。 这项工作将为理解众包的局限性奠定基础;特别是，众包的2D到3D算法将测试与新手创建3D形状有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.