Automatic Recognition and Segmentation of Architectural Elements from 2D Drawings by Convolutional Neural Network

Xiao, Yahan; Chen, Sen; Ikeda, Yasushi; Hotta, Kensuke

doi:10.52842/conf.caadria.2020.1.843

Cited by 2 publications

(2 citation statements)

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“…Some studies identified objects on 2D floor plans using semantic segmentation. Xiao et al [11] cropped the original 2D drawings into smaller image dimensions for feeding into a neural network model. They manually did pixel-level labeling on 300 2D drawings and implemented transfer learning from ResNet-152.…”

Section: Processing 2d Floor Plans Using Object Detection and Instanc...mentioning

confidence: 99%

Interoperability between Deep Neural Networks and 3D Architectural Modeling Software: Affordances of Detection and Segmentation

Wei,

Gupta,

Czerniawski

2023

Buildings

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Building owners are working on converting their legacy documentation 2D floor plans into digital 3D representations, but the manual process is labor-intensive and time-consuming. In this paper, deep learning is leveraged to automate the process. This automation requires interoperability between artificial neural networks and prevailing 3D modeling software. The system processes 2D floor plans and outputs parameters of recognized walls, single doors, double doors, and columns. The parameters include the start point and end point of the wall and the center point of the door and column. These parameters are input into Revit 2022 through the Revit API 2022 after post-processing. The dimensional parameter integration affordances of object detection and instance segmentation are studied and compared using Faster R-CNN and Mask R-CNN models. Instance segmentation was found to require more time for data labeling but was more capable of informing the modeling of irregularly shaped objects. The mean Average Precision (mAP) of object detection and instance segmentation are 71.7% and 69.3%, respectively. Apart from single doors, the average precision for other categories falls within the range of 74% to 96%. The results provide software developers with guidance on choosing between object detection and instance segmentation strategies for processing legacy building documents. These types of systems are anticipated to be pivotal to the industry’s transition from 2D to 3D information modalities and advise practitioners to carefully choose suitable models and consider the recommendations provided in this study to mitigate potential failure cases.

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Section: Processing 2d Floor Plans Using Object Detection and Instanc...mentioning

confidence: 99%

Interoperability between Deep Neural Networks and 3D Architectural Modeling Software: Affordances of Detection and Segmentation

Wei,

Gupta,

Czerniawski

2023

Buildings

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“…17 Generative Adversarial CNNs 18 were used to generate new, Islamic, geometric patterns based on provided examples 19 and a CNN was trained to distinguish architectural plans from sections. 20 The more specific, atomistic approaches include pixel-level classification 21 of architectural elements represented in architectural drawings, 22 generation of floor plan connectivity diagrams for the early phases of conceptual design, 23 and extraction and classification of the tectonic space types 24 based on the isovist representations. 25…”

Section: Introductionmentioning

confidence: 99%

Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

Dzieduszyński¹

2022

International Journal of Architectural Computing

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A substantial part of architectural and urban design involves processing of compositional interdependencies and contexts. This article attempts to isolate the problem of spatial composition from the broader category of synthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenarios varying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation of context-fitting spatial composition. The technique can be applied for the extraction of compositional principles from the architectural, urban, or artistic contexts and may facilitate the design-related decision making by complementing the required expert analysis.

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Automatic Recognition and Segmentation of Architectural Elements from 2D Drawings by Convolutional Neural Network

Cited by 2 publications

References 0 publications

Interoperability between Deep Neural Networks and 3D Architectural Modeling Software: Affordances of Detection and Segmentation

Interoperability between Deep Neural Networks and 3D Architectural Modeling Software: Affordances of Detection and Segmentation

Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

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