Proceedings ArticleDOI
DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans
Divya Sharma,Nitin Gupta,Chiranjoy Chattopadhyay,Sameep Mehta +3 more
- pp 420-425
TLDR
This paper proposes Deep Architecture for fiNdIng alikE Layouts (DANIEL), a novel deep learning framework to retrieve similar floor plan layouts from repository and creation of a new complex dataset ROBIN, having three broad dataset categories with 510 real world floor plans.Abstract:
Automatically finding out existing building layouts from a repository is always helpful for an architect to ensure reuse of design and timely completion of projects. In this paper, we propose Deep Architecture for fiNdIng alikE Layouts (DANIEL). Using DANIEL, an architect can search from the existing projects repository of layouts (floor plan), and give accurate recommendation to the buyers. DANIEL is also capable of recommending the property buyers, having a floor plan image, the corresponding rank ordered list of alike layouts. DANIEL is based on the deep learning paradigm to extract both low and high level semantic features from a layout image. The key contributions in the proposed approach are: (i) novel deep learning framework to retrieve similar floor plan layouts from repository; (ii) analysing the effect of individual deep convolutional neural network layers for floor plan retrieval task; and (iii) creation of a new complex dataset ROBIN (Repository Of BuildIng plaNs), having three broad dataset categories with 510 real world floor plans.We have evaluated DANIEL by performing extensive experiments on ROBIN and compared our results with eight different state-of-the-art methods to demonstrate DANIEL’s effectiveness on challenging scenarios.read more
Citations
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Journal ArticleDOI
Generation of hospital emergency department layouts based on generative adversarial networks
TL;DR: The analysis of the three GANs’ results shows that these models can autonomously generate new ED function layouts, of which the DCGAN results are the most flexible but the image quality is not ideal.
Proceedings ArticleDOI
Plan2Text: A framework for describing building floor plan images from first person perspective
TL;DR: It is demonstrated that the proposed end-to-end framework for first person vision based textual description synthesis of building floor plans gives state of the art performance on challenging, real-world floor plan images.
Journal ArticleDOI
A rotation and scale invariant approach for multi-oriented floor plan image retrieval
TL;DR: In this paper, a geometric feature-based approach for floor plan image retrieval is proposed, which is divided into three phases, namely outer shape feature extraction, internal object feature extraction and matching and retrieval.
Proceedings ArticleDOI
REXplore: A Sketch Based Interactive Explorer for Real Estates Using Building Floor Plan Images
TL;DR: REXplore is proposed, a novel framework that uses sketch based query mode to retrieve corresponding similar floor plan images from a repository using Cyclic Generative Adversarial Networks (Cyclic GAN) for mapping between sketch and image domain.
Journal ArticleDOI
Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans
TL;DR: A floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes and is a generative model.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
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