Sketching is a foundational step in the design process. Decades of sketch processing research have produced algorithms for 3D shape interpretation, beautification, animation generation, colorization, etc. However, there is a mismatch between sketches created in the wild and the clean, sketch-like input required by these algorithms, preventing their adoption in practice. The recent flurry of sketch vectorization, simplification, and cleanup algorithms could be used to bridge this gap. However, they differ wildly in the assumptions they make on the input and output sketches. We present the first benchmark to evaluate and focus sketch cleanup research. Our dataset consists of 281 sketches obtained in the wild and a curated subset of 101 sketches. For this curated subset along with 40 sketches from previous work, we commissioned manual vectorizations and multiple ground truth cleaned versions by professional artists. The sketches span artistic and technical categories and were created by a variety of artists with different styles. Most sketches have Creative Commons licenses; the rest permit academic use. Our benchmark's metrics measure the similarity of automatically cleaned rough sketches to artist-created ground truth; the ambiguity and messiness of rough sketches; and low-level properties of the output parameterized curves. Our evaluation identifies shortcomings among state-of-the-art cleanup algorithms and discusses open problems for future research.

中文翻译：

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粗略草图清理的基准

草图是设计过程中的基础步骤。数十年的草图处理研究已经产生了用于 3D 形状解释、美化、动画生成、着色等的算法。但是，在野外创建的草图与这些算法所需的干净、类似草图的输入之间存在不匹配，从而阻碍了它们的采用在实践中。最近出现的一系列草图矢量化、简化和清理算法可用于弥合这一差距。但是，它们在输入和输出草图上所做的假设大相径庭。我们提出了第一个基准来评估和关注草图清理研究。我们的数据集包括在野外获得的 281 个草图和 101 个草图的精选子集。对于这个精选的子集以及以前工作中的 40 幅草图，我们委托专业艺术家进行手动矢量化和多个地面实况清理版本。这些草图跨越了艺术和技术类别，由不同风格的各种艺术家创作。大多数草图都有知识共享许可；其余的允许学术使用。我们的基准指标衡量自动清理的粗略草图与艺术家创建的基本事实的相似性；粗略草图的模棱两可和混乱；和输出参数化曲线的低级属性。我们的评估确定了最先进的清理算法中的缺点，并讨论了未来研究的未解决问题。大多数草图都有知识共享许可；其余的允许学术使用。我们的基准指标衡量自动清理的粗略草图与艺术家创建的基本事实的相似性；粗略草图的模棱两可和混乱；和输出参数化曲线的低级属性。我们的评估确定了最先进的清理算法中的缺点，并讨论了未来研究的未解决问题。大多数草图都有知识共享许可；其余的允许学术使用。我们的基准指标衡量自动清理的粗略草图与艺术家创建的基本事实的相似性；粗略草图的模棱两可和混乱；和输出参数化曲线的低级属性。我们的评估确定了最先进的清理算法中的缺点，并讨论了未来研究的未解决问题。