Improved Automatic Analysis of Architectural Floor Plans

doi:10.1109/ICDAR.2011.177

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Improved Automatic Analysis of Architectural Floor Plans

Proceedings Article•DOI•

Improved Automatic Analysis of Architectural Floor Plans

Sheraz Ahmed¹, Marcus Liwicki¹, Markus Weber¹, Andreas Dengel¹•Institutions (1)

German Research Centre for Artificial Intelligence¹

18 Sep 2011-pp 864-869

TL;DR: This paper proposes a novel complete system for automated floor plan analysis that outperforms previous systems and introduces novel preprocessing methods, e.g., the differentiation between thick, medium, and thin lines and the removal of components outside the convex hull of the outer walls.

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Abstract: This paper proposes a novel complete system for automated floor plan analysis. Besides applying and improving state-of-the-art processing methods, we introduce novel preprocessing methods, e.g., the differentiation between thick, medium, and thin lines and the removal of components outside the convex hull of the outer walls. Especially the latter method increases the performance of the final system. In our experiments on a reference data set we compare our approach to other approaches available in the literature. We show that our system outperforms previous systems. The final room recognition accuracy is 79 % that is 10 % higher than the 69 % achieved by a state-of-the-art approach from the literature.

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Proceedings Article•DOI•

Raster-to-Vector: Revisiting Floorplan Transformation

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Chen Liu¹, Jiajun Wu², Pushmeet Kohli³, Yasutaka Furukawa¹•Institutions (3)

Washington University in St. Louis¹, Massachusetts Institute of Technology², Google³

01 Oct 2017

TL;DR: This paper addresses the problem of converting a rasterized floorplan image into a vector-graphics representation by adopting a learning-based approach and significantly outperforms existing methods and achieves around 90% precision and recall.

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Abstract: This paper addresses the problem of converting a rasterized floorplan image into a vector-graphics representation. Unlike existing approaches that rely on a sequence of lowlevel image processing heuristics, we adopt a learning-based approach. A neural architecture first transforms a rasterized image to a set of junctions that represent low-level geometric and semantic information (e.g., wall corners or door end-points). Integer programming is then formulated to aggregate junctions into a set of simple primitives (e.g., wall lines, door lines, or icon boxes) to produce a vectorized floorplan, while ensuring a topologically and geometrically consistent result. Our algorithm significantly outperforms existing methods and achieves around 90% precision and recall, getting to the range of production-ready performance. The vector representation allows 3D model popup for better indoor scene visualization, direct model manipulation for architectural remodeling, and further computational applications such as data analysis. Our system is efficient: we have converted hundred thousand production-level floorplan images into the vector representation and generated 3D popup models.

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127 citations

Cites background or methods from "Improved Automatic Analysis of Arch..."

...Detected lines are used to identify walls, where existing approaches require various heuristics, such as convex hull approximation, polygonal approximation, edge-linking to overcome gaps, or the color analysis along lines [18, 6]....
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...Morphological operations, Hough transform, or image vectorization techniques are used to extract lines, normalize line width, or group them into thin/medium/thick lines [6, 11]....
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...We have implemented the algorithm in [6] as a baseline....
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...Doors and windows exist in walls, and are detected by either looking at the openings on walls [6] or symbols spotting techniques [18]....
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Journal Article•DOI•

Statistical segmentation and structural recognition for floor plan interpretation

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Lluís-Pere de las Heras, Sheraz Ahmed¹, Marcus Liwicki¹, Ernest Valveny, Gemma Sánchez - Show less +1 more•Institutions (1)

German Research Centre for Artificial Intelligence¹

01 Sep 2014-International Journal on Document Analysis and Recognition

TL;DR: The proposed approach is able to analyze any type of floor plan regardless of the notation used and could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.

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Abstract: A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.

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88 citations

Cites background or methods or result from "Improved Automatic Analysis of Arch..."

...The performance of the system can be directly compared with the systems described in [2] and [27] on BlackSet, since only the 80 images from this dataset used in the evaluation of these methods are considered here....
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...They are compared with the recent works on floor plan analysis in [2] and in floor plan wall detection in [13]....
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...Several sub-problems related to floor plan analysis have been investigated, ranging from simple raster to vector conversion [20,34] to the full interpretation of the whole plan [10,26,37] going through intermediate tasks such as symbol recognition [24], symbol spotting in large libraries [11] or room detection and segmentation [2,27]....
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...Since [2] is based on thick, medium, and thin line separation for walls detection, the method is useless in its baseline for textured wall segmentation, as it is the case of TexturedSet, TexturedSet2 and ParallelSet....
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...In addition to that, some of the recently presented works on room detection [2,15,27] assumed that all floor plan images have the same resolution and line thickness....
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Proceedings Article•DOI•

Automatic Room Detection and Room Labeling from Architectural Floor Plans

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Sheraz Ahmed¹, Marcus Liwicki¹, Markus Weber¹, Andreas Dengel¹•Institutions (1)

German Research Centre for Artificial Intelligence¹

27 Mar 2012

TL;DR: An automatic system for analyzing and labeling architectural floor plans that could clearly outperform other state-of-the-art approaches for room detection and split rooms into several sub-regions if several semantic rooms share the same physical room.

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Abstract: This paper presents an automatic system for analyzing and labeling architectural floor plans. In order to detect the locations of the rooms, the proposed systems extracts both, structural and semantic information from given floor plans. Furthermore, OCR is applied on the text layer to retrieve the meaningful room labeling. Finally, a novel post-processing is proposed to split rooms into several sub-regions if several semantic rooms share the same physical room. Our fully automatic system is evaluated on a publicly available dataset of architectural floor plans. In our experiments, we could clearly outperform other state-of-the-art approaches for room detection.

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81 citations

Cites background or methods from "Improved Automatic Analysis of Arch..."

...This paper presents an extension of the work presented in [8] with an emphasis on semantic analysis....
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...The focus of this paper is on semantic analysis, for more details on structural analysis see [8]....
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...Further information about the existing approach can be found in [8]....
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...To extract these edges convex/concave hypothesis [8] is used....
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Journal Article•DOI•

Automatic reconstruction of 3D building models from scanned 2D floor plans

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Lucile Gimenez¹, Sylvain Robert¹, Frédéric Suard¹, Khaldoun Zreik•Institutions (1)

French Alternative Energies and Atomic Energy Commission¹

01 Mar 2016-Automation in Construction

TL;DR: In this article, the authors present a research work aiming at the development of methods for the generation of 3D building models from 2D plans, which can be used to accelerate the renovation of existing buildings.

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77 citations

Journal Article•DOI•

Automatic structural scene digitalization

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Rui Tang, Yuhan Wang, Darren Cosker¹, Wenbin Li²•Institutions (2)

University of Bath¹, Imperial College London²

17 Nov 2017-PLOS ONE

TL;DR: An automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format that applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets is presented.

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Abstract: In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.

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76 citations

Cites methods from "Improved Automatic Analysis of Arch..."

...That method was then adopted and expanded [11] to include new processing steps, such as wall edge extraction and boundary detection....
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Journal Article•DOI•

Speeded-Up Robust Features (SURF)

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Herbert Bay¹, Andreas Ess¹, Tinne Tuytelaars², Luc Van Gool¹•Institutions (2)

ETH Zurich¹, Katholieke Universiteit Leuven²

01 Jun 2008-Computer Vision and Image Understanding

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.

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12,449 citations

Journal Article•DOI•

Topological Structural Analysis of Digitized Binary Images by Border Following

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Satoshi Suzuki¹, Keiichi Abe¹•Institutions (1)

Shizuoka University¹

01 Apr 1985-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing

TL;DR: Two border following algorithms are proposed for the topological analysis of digitized binary images, which determine the surroundness relations among the borders of a binary image and follow only the outermost borders.

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Abstract: Two border following algorithms are proposed for the topological analysis of digitized binary images. The first one determines the surroundness relations among the borders of a binary image. Since the outer borders and the hole borders have a one-to-one correspondence to the connected components of 1-pixels and to the holes, respectively, the proposed algorithm yields a representation of a binary image, from which one can extract some sort of features without reconstructing the image. The second algorithm, which is a modified version of the first, follows only the outermost borders (i.e., the outer borders which are not surrounded by holes). These algorithms can be effectively used in component counting, shrinking, and topological structural analysis of binary images, when a sequential digital computer is used.

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2,303 citations

"Improved Automatic Analysis of Arch..." refers methods in this paper

...Subsequently, in our proposed system contours of the walls image are extracted using the method proposed by [19], i....
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Journal Article•DOI•

A robust algorithm for text string separation from mixed text/graphics images

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Lloyd Alan Fletcher¹, Rangachar Kasturi¹•Institutions (1)

Pennsylvania State University¹

01 Nov 1988-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: The development and implementation of an algorithm for automated text string separation that is relatively independent of changes in text font style and size and of string orientation are described and showed superior performance compared to other techniques.

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Abstract: The development and implementation of an algorithm for automated text string separation that is relatively independent of changes in text font style and size and of string orientation are described. It is intended for use in an automated system for document analysis. The principal parts of the algorithm are the generation of connected components and the application of the Hough transform in order to group components into logical character strings that can then be separated from the graphics. The algorithm outputs two images, one containing text strings and the other graphics. These images can then be processed by suitable character recognition and graphics recognition systems. The performance of the algorithm, both in terms of its effectiveness and computational efficiency, was evaluated using several test images and showed superior performance compared to other techniques. >

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664 citations

"Improved Automatic Analysis of Arch..." refers methods in this paper

...Our proposed method is based on [5] and [6]....
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...For the purpose of technical drawings, [5] proposed a method to extract text strings from mixed text/graphics images....
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...We split the image into two layers as in [5]....
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Journal Article•DOI•

Pattern recognition with moment invariants: a comparative study and new results

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Saeid Belkasim¹, Malayappan Shridhar¹, Majid Ahmadi¹•Institutions (1)

University of Windsor¹

01 Dec 1991-Pattern Recognition

TL;DR: The authors show that the new method of deriving Zernike moment invariants along with the new normalization scheme yield the best overall performance even when the data are degraded by additive noise.

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532 citations

Journal Article•DOI•

Symbol recognition by error-tolerant subgraph matching between region adjacency graphs

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Josep Lladós, Enric Martí, J.J. Villanueva

01 Oct 2001-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: An error-tolerant subgraph isomorphism algorithm formulated in terms of region adjacency graphs, which allows matching computing under distorted inputs and also reaching a solution in a near polynomial time.

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Abstract: We propose an error-tolerant subgraph isomorphism algorithm formulated in terms of region adjacency graphs (RAG). A set of edit operations to transform one RAG into another one are defined as regions are represented by polylines and string matching techniques are used to measure their similarity. The algorithm follows a branch and bound approach driven by the RAG edit operations. This formulation allows matching computing under distorted inputs and also reaching a solution in a near polynomial time. The algorithm has been used for recognizing symbols in hand drawn diagrams.

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232 citations