The maintenance, archiving and usage of the design drawings is cumbersome in physical form in different industries for longer period. It is hard to extract information by simple scanning of drawing sheets. Converting them to their digital formats such as Computer-Aided Design (CAD), with needed knowledge extraction can solve this problem. The conversion of these machine drawings to its digital form is a crucial challenge which requires advanced techniques. This research proposes an innovative methodology utilizing Deep Learning methods. The approach employs object detection model, such as Yolov7, Faster R-CNN, to detect physical drawing objects present in the images followed by, edge detection algorithms such as canny filter to extract and refine the identified lines from the drawing region and curve detection techniques to detect circle. Also ornaments (complex shapes) within the drawings are extracted. To ensure comprehensive conversion, an Optical Character Recognition (OCR) tool is integrated to identify and extract the text elements from the drawings. The extracted data which includes the lines, shapes and text is consolidated and stored in a structured comma separated values(.csv) file format. The accuracy and the efficiency of conversion is evaluated. Through this, conversion can be automated to help organizations enhance their productivity, facilitate seamless collaborations and preserve valuable design information in a digital format easily accessible. Overall, this study contributes to the advancement of CAD conversions, providing accurate results from the translating process. Future research can focus on handling diverse drawing types, enhanced accuracy in shape and line detection and extraction.
Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawings
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Guangzhou University（广州大学）；University of Science and Technology of China（中国科学技术大学）
Technical University of Munich（慕尼黑工业大学）；Alibaba Inc.（阿里巴巴集团）；Simon Fraser University（西蒙弗雷泽大学）
Spotting graphical symbols from the computer-aided design (CAD) drawings is essential to many industrial applications. Different from raster images, CAD drawings are vector graphics consisting of geometric primitives such as segments, arcs, and circles. By treating each CAD drawing as a graph, we propose a novel graph attention network GAT-CADNet to solve the panoptic symbol spotting problem: vertex features derived from the GAT branch are mapped to semantic labels, while their attention scores are cascaded and mapped to instance prediction. Our key contributions are three-fold: 1) the instanc…
ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting
Tongji University（同济大学）；Arcplus East China Architectural Design & Research Institute Co Ltd（Arcplus东中国建筑设计与研究有限公司）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Qi Zhi Institute（上海启智研究院）；University of Science and Technology of China（中国科学技术大学）；Shanghai Jiao Tong University（上海交通大学）；Donghua University（东华大学）
Access to large and diverse computer-aided design (CAD) drawings is critical for developing symbol spotting algorithms. In this paper, we present FloorPlanCAD, a large-scale real-world CAD drawing dataset containing over 10,000 floor plans, ranging from residential to commercial buildings. CAD drawings in the dataset are all represented as vector graphics, which enable us to provide line-grained annotations of 30 object categories. Equipped by such annotations, we introduce the task of panoptic symbol spotting, which requires to spot not only instances of countable things, but also the semanti…
Text-Enhanced Panoptic Symbol Spotting in CAD Drawings
Railway Siyuan Survey and Design Group Co., Ltd.（铁路硅谷勘察设计集团有限公司）；School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通运输科学与工程学院）
Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
Faculty of Information Technology, Beijing University of Technology（北京工业大学信息技术学院）；Software Development Center, China Information Technology Designing and Consulting Institute Co., Ltd（中国电子工程设计院有限公司软件开发中心）
Text-Aided Multi-Modal Panoptic Symbol Spotting for CAD Floor Plan Drawings
CCSE Lab, Beihang University（北京航空航天大学CCSE实验室）；School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通科学与工程学院）；The Hong Kong Polytechnic University（香港理工大学）
Point or Line? Using Line-based Representation for Panoptic Symbol Spotting in CAD Drawings
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Tongji University（同济大学）；Donghua University（东华大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Arcplus East China Architectural Design & Research Institute Co., Ltd.（上海弧plus建筑设计与研究有限公司）
Gwangju Institute of Science and Technology (GIST)（光州科学技术院）
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline that can detect the injection parts such as hook, boss, undercut and press parts such as DPS, Embo-Screwless, Embo-Burring, and EMBO in the 2D CAD drawing images. We first cropped the drawing to a specific size for the training efficiency of a deep neural network. Then, we use Cascade R-CNN to find the position of injection and press parts and use Resnet-50 to predict the orientation of the parts. Finally, we convert…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；New York University（纽约大学）
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperform…
Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses sig…
In this paper, we introduce a new approach for retrieval and classification of 3D models that directly performs in the Computer-Aided Design (CAD) format without any conversion to other representations like point clouds or meshes, thus avoiding any loss of information. Among the various CAD formats, we consider the widely used STEP extension, which represents a standard for product manufacturing information. This particular format represents a 3D model as a set of primitive elements such as surfaces and vertices linked together. In our approach, we exploit the linked structure of STEP files to…
This paper addresses the issue of autonomously detecting text on technical drawings. The detection of text on technical drawings is a critical step towards autonomous production machines, especially for brown-field processes, where no closed CAD-CAM solutions are available yet. Automating the process of reading and detecting text on technical drawings reduces the effort for handling inefficient media interruptions due to paper-based processes, which are often todays quasi-standard in brown-field processes. However, there are no reliable methods available yet to solve the issue of automatically…
Manycore Tech Inc.（Manycore科技公司）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of California, Berkeley（加州大学伯克利分校）；Zhejiang University（浙江大学）
In this paper, we revisit the long-standing problem of automatic reconstruction of 3D objects from single line drawings. Previous optimization-based methods can generate compact and accurate 3D models, but their success rates depend heavily on the ability to (i) identifying a sufficient set of true geometric constraints, and (ii) choosing a good initial value for the numerical optimization. In view of these challenges, we propose to train deep neural networks to detect pairwise relationships among geometric entities (i.e., edges) in the 3D object, and to predict initial depth value of the vert…
University of Washington（华盛顿大学）；Adobe Research（奥多比研究院）
The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep). Several data sets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large scale databases are largely unlabeled, and labeled datasets are small. Additionally, task specific label sets are rare, and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning ta…
University of California, Berkeley（加州大学伯克利分校）；Manycore Tech Inc.（酷家乐科技有限公司）
In computer-aided design (CAD) systems, 2D line drawings are commonly used to illustrate 3D object designs. To reconstruct the 3D models depicted by a single 2D line drawing, an important key is finding the edge loops in the line drawing which correspond to the actual faces of the 3D object. In this paper, we approach the classical problem of face identification from a novel data-driven point of view. We cast it as a sequence generation problem: starting from an arbitrary edge, we adopt a variant of the popular Transformer model to predict the edges associated with the same face in a natural o…
PlankAssembly: Robust 3D Reconstruction from Three Orthographic Views with Learnt Shape Programs
Sun Yat-Sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；Manycore Tech Inc.（众核科技公司）；University of Electronic Science and Technology of China（电子科技大学）
TU Berlin（柏林工业大学）；Skoltech（斯科尔科沃科学技术研究所）；IITP（信息传输问题研究所）；New York University（纽约大学）
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a l…
CADmium: Fine-Tuning Code Language Models for Text-Driven Sequential CAD Design
Chandar Research Lab（昌德拉研究实验室）；Mila – Quebec AI Institute（魁北克人工智能研究所）；Polytechnique Montréal（蒙特利尔理工学院）；Ansys（安世仿真）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice. For example, a large dialog LLM like ChatGPT has successfully passed part of the US medical licensing exam. However, LLMs currently have difficulty processing images, making it challenging to interpret information from medical images, which are rich in information that supports clinical decisions. On the other hand, computer-aided diagnosis (CAD) networks for medical images have seen significant success in the medical field by using advanced deep-le…
