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）是一个数十亿美元的行业，负责几乎所有制造商品的数字设计。它利用参数建模，可以更改设计的尺寸，从而促进基于物理的优化和通过非专家重新混合设计。但是CAD的潜力降低了创建参数模型的困难：除了掌握设计原理外，专业人员还必须学习复杂的CAD软件接口。为了促进有效的建模策略和创造性流程，设计教育者倡导徒手绘图，作为参数建模的初步步骤。不幸的是，CAD系统不了解这些图纸，因此设计人员必须使用复杂的CAD软件重新创建整个设计。该研究项目探讨了一个问题：“是否可以将徒手图纸自动转换为参数CAD模型？”通过利用通过绘画和CAD建模共享的视觉词汇，该项目将开发一个系统，从绘画的自然语言转换为CAD的形式语言。这项技术将提高多个行业的专业CAD设计师的生产率，并使更多的人可以使用CAD建模，而无需进行大量混淆软件界面的培训。为了将图纸作为输入，研究人员将把它们视为时间戳序列的中风序列，从而使他们能够将问题作为来自CAD Progence to Drawe Stroke to cad Programe to cad programe token token token token secoress的机器翻译中的一个问题。绘图笔划分组为与CAD建模策略相关的连贯绘图操作（例如，首先绘制构造线和简单的原始形状，然后进行完善）。研究人员建议将这些图纸操作提取为中间表示，这有助于在许多可以代表单个形状的程序（可能无限地）之间消除歧义。进行此提取然后生产CAD程序是复杂的搜索问题；研究人员将利用新颖的深神经网络来指导搜索。他们将从专业设计人员那里收集一个配对（绘图，CAD程序）数据集，以帮助开发这些网络。他们还将开发不需要此类基真实数据的学习算法。最后，他们将开发指标来评估该系统制定的CAD计划，该计划将既用于评估系统的效率并指导计划搜索过程。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的审查标准通过评估来评估的。