CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool
01 Mar 2015-International Journal on Document Analysis and Recognition (Springer Berlin Heidelberg)-Vol. 18, Iss: 1, pp 15-30
TL;DR: This paper presents a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations and implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner.
Abstract: Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research.
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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.
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.
127 citations
Cites background from "CVC-FP and SGT: a new database for ..."
...[12] combined multiple existing datasets to acquire vector-graphics representation groundtruth for 122 floorplans....
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08 May 2017
TL;DR: A method for analyzing floor plan images using wall segmentation, object detection, and optical character recognition, and fully convolutional networks (FCN) is introduced and applications in automatic 3D model building and interactive furniture fitting are shown.
Abstract: This paper introduces a method for analyzing floor plan images using wall segmentation, object detection, and optical character recognition. We introduce a challenging new real-estate floor plan dataset, R-FP, evaluate different wall segmentation methods, and propose fully convolutional networks (FCN) for this task. We explore architectures with different pixel-stride values and more compact ones with skipped pooling layers. An FCN-2s with a 2-pixel stride layer achieves state-of-the-art performance, obtaining a mean Intersection-over-Union score of 89.9% on R-FP, and 94.4% on the public CVC-FP data set. Using OCR and object detection, we estimate room sizes. Finally, we show applications in automatic 3D model building and interactive furniture fitting.
64 citations
Cites methods from "CVC-FP and SGT: a new database for ..."
...We also evaluate on the publicly available CVC floor plan data set [5]....
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11 Jun 2019
TL;DR: 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, and presents a method relying on an improved multi-task convolutional neural network.
Abstract: 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. Data and code at: https://github.com/CubiCasa/CubiCasa5k.
42 citations
01 Nov 2017
TL;DR: 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.
41 citations
Additional excerpts
...Also, in the CVC-FP dataset, the samples are insufficient in number for the task of floor plan retrieval....
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...They are: (i) SESYD [24] and (ii) CVC-FP [25]....
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01 Dec 2016
TL;DR: A framework for the matching and retrieval of similar architectural floorplans under the query by example paradigm is proposed and a novel graph spectral embedding feature is proposed to uniquely represent the layout of the architectural floorplan.
Abstract: An automatic lookup tool, which matches and retrieves similar floorplans from a large repository of digitized architectural floorplans can prove to be of immense help for the architects while designing new projects. In this paper, we have proposed a framework for the matching and retrieval of similar architectural floorplans under the query by example paradigm. We propose a room layout segmentation and adjacent room detection algorithm to represent layouts as an undirected graph. We have also proposed a novel graph spectral embedding feature to uniquely represent the layout of the architectural floorplan. This helps in effective and efficient matching of the room layouts. Room semantics in terms of both the room structures and room decor is used to retrieve similar floorplans from the repository. To match the semantic similarity between a pair of floorplans, we have proposed a two stage matching technique. We have validated the effectiveness of our proposed framework by performing experiments on publicly available floorplan dataset and achieved high retrieval accuracy.
19 citations
Cites methods from "CVC-FP and SGT: a new database for ..."
...Segmentation results on two images from CVC-FP [17] dataset: (a) Input floor plan, (b) Technique proposed in [18], (c) Our approach Query Layout Feature Extraction...
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References
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TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Abstract: The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
15,935 citations
"CVC-FP and SGT: a new database for ..." refers methods in this paper
...The JI score is used in PASCAL VOC competitions for object segmentation [20], and it counts the mislabeled pixels in the image....
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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.
12,449 citations
"CVC-FP and SGT: a new database for ..." refers methods in this paper
...Here, doors and windows—which are represented by arcs—are detected using the SURF detector [9]....
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TL;DR: Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach to combining first-order logic and probabilistic graphical models in a single representation.
Abstract: We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.
2,916 citations
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...In the field of document image analysis, there is a long experience on structural methods for information extraction and analysis of multiple types of documents [21,29,37]....
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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.
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.
2,303 citations