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Proceedings ArticleDOI

Automatic Room Detection and Room Labeling from Architectural Floor Plans

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TLDR
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.

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Citations
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Proceedings ArticleDOI

Room segmentation: Survey, implementation, and analysis

TL;DR: This paper surveys the literature on room segmentation and provides four publicly available implementations of popular methods, which target the semantic mapping domain and are tuned to yield segmentations into complete rooms.
Journal ArticleDOI

Statistical segmentation and structural recognition for floor plan interpretation

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.
Proceedings ArticleDOI

Parsing floor plan images

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.

Automatic generation of structural building descriptions from 3D point cloud scans

TL;DR: This work presents a new method for automatic semantic structuring of 3D point clouds representing buildings that focuses on the building's interior using indoor scans to derive high-level architectural entities like rooms and doors.
Proceedings ArticleDOI

DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans

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.
References
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Empirical performance evaluation of graphics recognition systems

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Journal ArticleDOI

A complete system for the analysis of architectural drawings

TL;DR: A complete system for the analysis of architectural drawings, with the aim of reconstructing in 3D the represented buildings, and a proposed 3D modeling process which matches reconstructed floors is presented.
Proceedings ArticleDOI

Improved Automatic Analysis of Architectural Floor Plans

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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