Many tasks in graphics and vision demand machinery for converting shapes into consistent representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguous, however, artists and engineers often manually construct such representations, a tedious and potentially time-consuming process. While advances in deep learning have been successfully applied to noisy geometric data, the task of generating parametric shapes has so far been difficult for these methods. Hence, we propose a new framework for predicting parametric shape primitives using deep learning. We use distance fields to transition between shape parameters like control points and input data on a pixel grid. We demonstrate efficacy on 2D and 3D tasks, including font vectorization and surface abstraction.

中文翻译：

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使用距离场的深度参数形状预测

图形和视觉中的许多任务需要机器将形状转换为具有稀疏参数集的一致表示；这些表示有助于渲染、编辑和存储。然而，当源数据嘈杂或不明确时，艺术家和工程师通常会手动构建此类表示，这是一个乏味且可能耗时的过程。虽然深度学习的进步已成功应用于嘈杂的几何数据，但迄今为止，这些方法难以生成参数形状。因此，我们提出了一个使用深度学习预测参数形状基元的新框架。我们使用距离场在形状参数（如控制点）和像素网格上的输入数据之间进行转换。我们展示了 2D 和 3D 任务的功效，包括字体矢量化和表面抽象。