Parsing floor plan images

Dodge, Samuel; Xu, Jianyu; Stenger, Björn

doi:10.23919/mva.2017.7986875

Cited by 90 publications

(99 citation statements)

References 10 publications

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“…This motivates the development of machine learning methods [4], and very recently, deep learning methods [5,11,20] to address the problem. Dodge et al [5] used a fully convolutional network (FCN) to first detect the wall pixels, and then adopted a faster R-CNN framework to detect doors, sliding doors, and symbols such as kitchen stoves and bathtubs. Also, they employed a library tool to recognize text to estimate the room size.…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al [11] designed a Figure 1. Our network is able to recognize walls of nonuniform thickness (see boxes 2,4,5), walls that meet at irregular junctions (see boxes 1,2), curved walls (see box 3), and various room types in the layout; see Figure 2 for the legend of the color labels. convolutional neural network (CNN) to recognize junction points in a floor plan image and connected the junctions to locate walls.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Floor Plan Recognition Using a Multi-Task Network With Room-Boundary-Guided Attention

Zeng

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

101

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This paper presents a new approach to recognize elements in floor plan layouts. Besides walls and rooms, we aim to recognize diverse floor plan elements, such as doors, windows and different types of rooms, in the floor layouts. To this end, we model a hierarchy of floor plan elements and design a deep multi-task neural network with two tasks: one to learn to predict room-boundary elements, and the other to predict rooms with types. More importantly, we formulate the room-boundary-guided attention mechanism in our spatial contextual module to carefully take room-boundary features into account to enhance the room-type predictions. Furthermore, we design a cross-and-within-task weighted loss to balance the multi-label tasks and prepare two new datasets for floor plan recognition. Experimental results demonstrate the superiority and effectiveness of our network over the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Floor Plan Recognition Using a Multi-Task Network With Room-Boundary-Guided Attention

Zeng

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

101

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show abstract

“…[7] CVC-FP Qgar project, Hough transform Entity segmentation [9] CVC-FP Predefined rule Room detection [10] CVC-FP Contour extraction Room detection [11] Defined in paper Predefined rule Room detection [12] CVC-FP SVM-BoVM, centerline detection Room detection [13] CVC-FP, R-FP Deep learning (FCN-2s) Pixel [14] R-FP Deep learning (ResNet-152) Vector with points and lines Note: FCN-2s: Fully Convolutional Network with stride 2; ResNet-152: Residual Network 152.…”

Section: Dataset Methods Resultsmentioning

confidence: 99%

“…Although the accuracy of generated data was quite low, this research was the first to try to generate vector data from various types of 2D floorplan images. Dodge et al [13] used FCN-2s to segment walls in addition to R-FP (Rakuten Floorplan) datasets for training to improve segmentation results. However, they did not conduct any postprocesses to construct indoor spatial information.…”

Section: Introductionmentioning

confidence: 99%

Indoor Reconstruction from Floorplan Images with a Deep Learning Approach

Jang

Yang

2020

IJGI

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Although interest in indoor space modeling is increasing, the quantity of indoor spatial data available is currently very scarce compared to its demand. Many studies have been carried out to acquire indoor spatial information from floorplan images because they are relatively cheap and easy to access. However, existing studies do not take international standards and usability into consideration, they consider only 2D geometry. This study aims to generate basic data that can be converted to indoor spatial information using IndoorGML (Indoor Geography Markup Language) thick wall model or the CityGML (City Geography Markup Language) level of detail 2 by creating vector-formed data while preserving wall thickness. To achieve this, recent Convolutional Neural Networks are used on floorplan images to detect wall and door pixels. Additionally, centerline and corner detection algorithms were applied to convert wall and door images into vector data. In this manner, we obtained high-quality raster segmentation results and reliable vector data with node-edge structure and thickness attributes that enabled the structures of vertical and horizontal wall segments and diagonal walls to be determined with precision. Some of the vector results were converted into CityGML and IndoorGML form and visualized, demonstrating the validity of our work.

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“…In summary, the era of deep neural networks has given rise to significantly better methods for 2D floorplan analysis. According to [13,6], especially fully convolutional CNNs have a huge potential in extracting accurate pixel-level geometric and semantic information that can be further utilized in later post-processing steps to construct more effective heuristics to restore the lost floorplan elements.…”

Section: Related Workmentioning

confidence: 99%

CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis

Kalervo

Ylioinas

Häikiö³

et al. 2019

Lecture Notes in Computer Science

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Better understanding and modelling of building interiors and the emergence of more impressive AR/VR technology has brought up the need for automatic parsing of floorplan images. However, there is a clear lack of representative datasets to investigate the problem further. To address this shortcoming, this paper presents a novel image dataset called CubiCasa5K, a large-scale floorplan image dataset containing 5000 samples annotated into over 80 floorplan object categories. The dataset annotations are performed in a dense and versatile manner by using polygons for separating the different objects. Diverging from the classical approaches based on strong heuristics and low-level pixel operations, we present a method relying on an improved multi-task convolutional neural network. By releasing the novel dataset and our implementations, this study significantly boosts the research on automatic floorplan image analysis as it provides a richer set of tools for investigating the problem in a more comprehensive manner.

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Parsing floor plan images

Cited by 90 publications

References 10 publications

Deep Floor Plan Recognition Using a Multi-Task Network With Room-Boundary-Guided Attention

Deep Floor Plan Recognition Using a Multi-Task Network With Room-Boundary-Guided Attention

Indoor Reconstruction from Floorplan Images with a Deep Learning Approach

CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis

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