CISE-ANR: HCC: Small: Learning to Translate Freehand Design Drawings into Parametric CAD Programs

CISE-ANR：HCC：小型：学习将手绘设计图转换为参数化 CAD 程序

• 批准号: 2315354
• 负责人: Daniel Ritchie
• 金额: $ 60万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315354&HistoricalAwards=false
• 关键词: CISE; ANR; HCC; Small; Learning

Computer Aided Design (CAD) is a multi-billion dollar industry responsible for the digital design of almost all manufactured goods. It leverages parametric modeling, which allows dimensions of a design to be changed facilitating physically-based optimization and design re-mixing by non-experts. But CAD’s potential is diminished by the difficulty of creating parametric models: in addition to mastering design principles, professionals must learn complex CAD software interfaces. To promote effective modeling strategies and creative flow, design educators advocate freehand drawing as a preliminary step to parametric modeling. Unfortunately, CAD systems do not understand these drawings, so designers must re-create their entire design using complex CAD software. This research project explores the question, "Is it possible to automatically convert freehand drawings to parametric CAD models?" By leveraging the visual vocabulary shared by drawing and CAD modeling, this project will develop a system to translate from the natural language of drawing to the formal language of CAD. This technology will increase the productivity of professional CAD designers across multiple industries and make CAD modeling accessible to more people without extensive training in confusing software interfaces.To handle drawings as input, the researchers will treat them as timestamped sequences of strokes, allowing them to cast the problem as one of machine translation from drawing stroke sequences to CAD program token sequences. Drawing strokes are grouped into coherent drawing operations that are correlated with CAD modeling strategies (e.g. first drawing construction lines and simple primitives shapes, then refining). The researchers propose to extract these drawing operations as an intermediate representation, which helps disambiguate between the (potentially infinitely) many programs which can represent a single shape. Performing this extraction and then producing CAD programs are complex search problems; the researchers will leverage novel deep neural networks to guide the search. They will gather a paired (drawing, CAD program) dataset from professional designers to help develop these networks. They will also develop learning algorithms that do not require such ground-truth paired data. Finally, they will develop metrics to assess CAD programs produced by the system, which will be used both to evaluate the system's efficacy and to guide the program search process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机辅助设计 (CAD) 是一个价值数十亿美元的行业，负责几乎所有制成品的数字设计，它利用参数化建模，允许更改设计的尺寸，从而促进基于物理的优化和非设计重新混合。但 CAD 的潜力因创建参数化模型的难度而减弱：除了掌握设计原理之外，专业人员还必须学习复杂的 CAD 软件界面，为了促进有效的建模策略和创意流程，设计教育者主张将手绘图作为实现这一目标的第一步。不幸的是，CAD 系统无法理解这些绘图，因此设计人员必须使用复杂的 CAD 软件重新创建整个设计，该研究项目探讨了“是否可以将徒手绘图自动转换为参数化 CAD 模型？”该项目将利用绘图和 CAD 建模共享的视觉词汇，开发一个将绘图的自然语言转换为 CAD 的形式语言的系统。该技术将提高跨多个行业的专业 CAD 设计师的生产力，并使 CAD 建模变得易于使用。给更多的人为了将绘图作为输入来处理，研究人员将它们视为带有时间戳的笔划序列，从而使他们能够将问题转化为从绘图笔划序列到 CAD 程序标记序列的机器翻译之一。研究人员建议将这些绘图操作提取为中间表示，这有助于消除（可能）之间的歧义。执行此提取然后生成 CAD 程序是复杂的搜索问题；研究人员将利用新颖的深度神经网络来指导搜索，他们将从中收集配对（绘图、CAD 程序）数据集。他们还将开发不需要此类真实配对数据的学习算法，最后，他们将开发评估系统生成的 CAD 程序的指标，这些指标将用于评估系统的功效。并指导程序搜索过程。这授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。