Enhanced Object Detection in Floor Plan Through Super-Resolution

Khare, Dev; Kamal, N. S.; Ganesh, H B Barathi; Sowmya, V.; Variyar, V. V. Sajith

doi:10.1007/978-981-19-5868-7_19

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Learning-based approaches: Learning-based approaches have been gaining popularity in the field of entity recognition in scanned drawings and consist of the use of deep learning for training a network to identify building components in technical drawings. Different types of networks have been used throughout the literature, including Graph Neural Networks (GNN) [18,39], Generative Adversarial Networks (GAN) [39,55], Convolutional Neural Networks (CNN) [56][57][58], Global Convolutional Networks (GCN) [59], Fully Convolutional Networks (FCN) [60], Faster Region-based Convolutional Neural Networks (Faster R-CNN) [25], Cascade Mask R-CNN [61,62] and ResNet-50 [63][64][65]. These networks rely on datasets containing large quantities of floor plans to train the network to produce reliable results.…”

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confidence: 99%

Automatic Reconstruction of 3D Models from 2D Drawings: A State-of-the-Art Review

Feist,

Jacques de Sousa,

Sanhudo

et al. 2024

Eng

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Among the methods of 3D reconstruction, the automatic generation of 3D models from building documentation is one of the most accessible and inexpensive. For 30 years, researchers have proposed multiple methods to automatically generate 3D models from 2D drawings. This study compiles this research and discusses the different methods used to generate 3D models from 2D drawings. It offers a critical review of these methods, focusing on the coverage and completeness of the reconstruction process. This review allows us to identify the research gaps in the literature, and opportunities for improvement are identified for future research.

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