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
More filters
Book ChapterDOI
3D Modelling Approach for Ancient Floor Plans' Quick Browsing
TL;DR: In this article , a U-net convolutional neural network is used to detect and localise walls in an input floor plan image using a statistical image segmentation model based on the U-Net CNN architecture and a binary wall mask image is obtained.
Proceedings ArticleDOI
Shape-Based Floor Plan Retrieval Using Parse Tree Matching
Philip K. Lee,Bjorn Stenger +1 more
TL;DR: In this article, the authors incorporate interior layout into the similarity metric using a tree structure that represents both the layout hierarchy and room shapes of the floor plan, and evaluate their similarity using an appropriately defined tree edit distance.
Journal ArticleDOI
An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design
TL;DR: In this article , an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout.
Journal ArticleDOI
RISC-Net : rotation invariant siamese convolution network for floor plan image retrieval
TL;DR: A deep learning-based model, Rotation Invariant Siamese Convolution Network (RISC-Net), which is able to retrieve similar floor plan images from the dataset, even in the presence of rotation, is proposed.
Journal ArticleDOI
An interactive approach for generating spatial architecture layout based on graph theory
Xiaoye Xie,Wowo Ding +1 more
TL;DR: In this article , a graph theory based method for generating three-dimensional architectural layouts is proposed, with the target of creating space that provides rich perceptual experience, incorporating the decisions of architects in the generation process.
References
More filters
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.
Posted Content
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.