A system to detect rooms in architectural floor plan images

Macé, Sébastien; Locteau, Hervé; Valveny, Ernest; Tabbone, Salvatore

doi:10.1145/1815330.1815352

Cited by 88 publications

(60 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two floor plans (A and B) used in the evaluation are shown in Figure 6. Both were generated by GraFloor from a floor plan in the corpus from [18] with English room names replacing the original French names. Two versions of each floor plan were created.…”

Section: Procedures and Materialsmentioning

confidence: 99%

See 1 more Smart Citation

Accessible On-Line Floor Plans

Goncu

Madugalla

Marinai³

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

Better access to on-line information graphics is a pressing need for people who are blind or have severe vision impairment. We present a new model for accessible presentation of on-line information graphics and demonstrate its use for presenting floor plans. While floor plans are increasingly provided on-line, people who are blind are at best provided with only a high-level textual description. This makes it difficult for them to understand the spatial arrangement of the objects on the floor plan. Our new approach provides users with significantly better access to such plans. The users can automatically generate an accessible version of a floor plan from an on-line floor plan image quickly and independently by using a web service. This generates a simplified graphic showing the rooms, walls, doors and windows in the original floor plan as well as a textual overview. The accessible floor plan is presented on an iPad using audio feedback. As the users touch graphic elements on the screen, the element they are touching is described by speech and non-speech audio in order to help them navigate the graphic.

show abstract

Section: Procedures and Materialsmentioning

confidence: 99%

“…The dataset [18] is composed of 90 images that contain floor plans with some basic furniture objects and rooms labelled with textual descriptions. These images have been provided by an architectural office and cover a period of more than ten years.…”

Section: Quality Of Automatic Transcriptionmentioning

confidence: 99%

Accessible On-Line Floor Plans

Goncu

Madugalla

Marinai³

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

show abstract

“…Also human feedback is used throughout the analysis phase. [18] proposed a method to detect rooms in the architectural floor plan images. This method is adopted and expanded in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…[17] and [18] used a thick/thin line separation algorithm for the separation of walls from the symbols. This algorithm separates the image into two images, i.e., a thick lines image containing the walls and a thin lines image containing the symbols.…”

Section: A Information Segmentationmentioning

confidence: 99%

Improved Automatic Analysis of Architectural Floor Plans

Ahmed

Liwicki

Weber

et al. 2011

2011 International Conference on Document Analysis and Recognition

View full text Add to dashboard Cite

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.

show abstract

“…However, they should be given special emphasis as the simplest and most unambiguous representation of information, especially for vital applications such as emergency planning and first respond. Traditionally the employment of floor plans is dependent on the availability of a Computer Aided Design (CAD) model, otherwise is restricted to the digitization from image documents (Macé et al, 2010) or sketch based retrieval (Ahmed et al, 2014) which already presuppose existing reliable plans. Not all buildings satisfy this requirement, both in terms of preservation of the blueprints, and faithful construction or renovation of the architectural design.…”

Section: Introductionmentioning

confidence: 99%

Towards Object Driven Floor Plan Extraction From Laser Point Cloud

Babacan

Jung

Wichmann

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:During the last years, the demand for indoor models has increased for various purposes. As a provisional step to proceed towards higher dimensional indoor models, powerful and flexible floor plans can be utilised. Therefore, several methods have been proposed that provide automatically generated floor plans from laser point clouds. The prevailing methodology seeks to attain semantic enhancement of a model (e.g. the identification and labelling of its components) built upon already reconstructed (a priori) geometry. In contrast, this paper demonstrates preliminary research on the possibility to directly incorporate semantic knowledge, which is itself derived from the raw data during the extraction, into the geometric modelling process. In this regard, we propose a new method to automatically extract floor plans from raw point clouds. It is based on a hierarchical space partitioning of the data, integrated with primitive selection actuated by object detection. First, planar primitives corresponding to vertical architectural structures are extracted using M-estimator SAmple and Consensus (MSAC). The set of the resulting line segments are refined by a selection process through a novel door detection algorithm, considering optimization of prior information and fitness to the data. The selected lines are used as hyperlines to partition the space into enclosed areas. Finally, a floor plan is extracted from these partitions by Minimum Description Length (MDL) hypothesis ranking. The algorithm is applied on a real mobile laser scanner dataset and the results are evaluated both in terms of door detection and consecutive floor plan extraction.

show abstract

A system to detect rooms in architectural floor plan images

Cited by 88 publications

References 17 publications

Accessible On-Line Floor Plans

Accessible On-Line Floor Plans

Improved Automatic Analysis of Architectural Floor Plans

Towards Object Driven Floor Plan Extraction From Laser Point Cloud

Contact Info

Product

Resources

About