DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans
01 Nov 2017-pp 420-425
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.
Citations
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23 Aug 2020
TL;DR: This work presents a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images, that quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.
Abstract: We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.
19 citations
Additional excerpts
..., SESYD [8], ROBIN [37], and FPLAN-POLY [34])....
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TL;DR: In insights about how document analysis systems are built, the examination of the practices of researchers in this field allows us to conclude that the tools that are used, and related issues, have become more and more complex over time.
18 citations
TL;DR: In this paper, a transformer-based network is used to estimate vector primitives and an optimization procedure is performed to obtain the final primitive configurations, which outperforms a number of existing techniques on a collection of representative technical drawings.
Abstract: We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.
17 citations
TL;DR: This paper investigates 10 relevant studies combining GSL and EPO and analyses their gaps and extends the analysis to the research on GSL andEPO.
16 citations
TL;DR: A novel algorithm to extract high-level semantic features from an architectural floor plan using weighted sum of the features is proposed, where a feature can be given more preference over others, during retrieval.
Abstract: Due to the massive growth of real estate industry, there is an increase in the number of online platforms designed for finding homes/furnished properties. Instead of descriptive words, query by example is always a preferred method for retrieval. Floor plans are the basic 2D representation giving an idea about the building structure at a particular level. The authors propose a framework for the retrieval of similar architectural floor plans under the query by example paradigm. They propose a novel algorithm to extract high-level semantic features from an architectural floor plan. Fine-grained retrieval using weighted sum of the features is proposed, where a feature can be given more preference over others, during retrieval. Experiments were performed on publicly available dataset containing 510 floor plans and compared with existing state-of-the-art techniques. Their proposed method outperforms others both in qualitative and quantitative terms.
12 citations
References
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09 Jun 2010
TL;DR: A primitive extraction algorithm based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection and the way it detects some door hypothesis thanks to the extraction of arcs is presented.
Abstract: In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results.
88 citations
"DANIEL: A Deep Architecture for Aut..." refers background in this paper
...Examples of other related work include (i) generating 3D models [4]; (ii) detecting rooms and their connectivity topology [5]; and (iii) sketch based retrieval [6]....
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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.
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.
81 citations
Additional excerpts
...An endto-end system is proposed in [11] that extracts the room labels to identify the functions of the rooms for the analysis of architectural diagrams....
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TL;DR: A new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context based on the definition of a set of constraints that permits to generate documents with different features that are reflected in variation of localization results.
Abstract: This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.
65 citations
"DANIEL: A Deep Architecture for Aut..." refers background or methods in this paper
...In [24], there are 10 different types of layouts, each with 100 images varying in their arrangement of furniture inside the rooms....
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...They are: (i) SESYD [24] and (ii) CVC-FP [25]....
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...We performed our experiments on SESYD [24] and ROBIN to show the effectiveness of DANIEL....
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...We have followed standard notations given in [24], for all the floor plans so that ROBIN can be used for other floor plan analysis tasks as well, and not only for retrieval....
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TL;DR: A sketch-based system, namely the a.SCatch system, for querying a floor plan repository, and a novel complete system for floor plan analysis, which extracts the semantics from existing floor plans.
53 citations
"DANIEL: A Deep Architecture for Aut..." refers background or methods in this paper
...Examples of other related work include (i) generating 3D models [4]; (ii) detecting rooms and their connectivity topology [5]; and (iii) sketch based retrieval [6]....
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...Another related method is the one proposed in [6], where a sketchbased similar floor plan retrieval approach was proposed to query the floor plan repository by categorizing areas in a floor plan as rooms, zones, units and levels....
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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.
52 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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