CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool

Heras, Lluís-Pere de las; Terrades, Oriol Ramos; Robles, Sergi; Sánchez, Gemma

doi:10.1007/s10032-014-0236-5

Cited by 59 publications

(37 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…us far, there are limited publicly available datasets that contain the architectural floor plan images. For instance, these four datasets: CVC-FP [26], SESYD [27], Robin [28], and Rent3D [29] usually served as the experimental data in academic studies. e detailed information of these datasets is shown in Table 1 and the sample images are illustrated in Figure 4.…”

Section: Dataset Descriptionmentioning

confidence: 99%

How Many Bedrooms Do You Need? A Real-Estate Recommender System from Architectural Floor Plan Images

Gan

Wang

Huang

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

This paper introduces an automated image processing method to analyze an architectural floor plan database. The floor plan information, such as the measurement of the rooms, dimension lines, and even the location of each room, can be automatically produced. This assists the real-estate agents to maximise the chances of the closure of deals by providing explicit insights to the prospective purchasers. With a clear idea about the layout of the place, customers can quickly make an analytical decision. Besides, it reduces the specialized training cost and increases the efficiency in business actions by understanding the property types with the greatest demand. Succinctly, this paper utilizes both the traditional image processing and convolutional neural networks (CNNs) to detect the bedrooms by undergoing the segmentation and classification processes. A thorough experiment, analysis, and evaluation had been performed to verify the effectiveness of the proposed framework. As a result, a three-class bedroom classification accuracy of ∼ 90% was achieved when validating on more than 500 image samples that consist of the different room numbers. In addition, qualitative findings were presented to manifest visually the feasibility of the algorithm developed.

show abstract

Section: Dataset Descriptionmentioning

confidence: 99%

How Many Bedrooms Do You Need? A Real-Estate Recommender System from Architectural Floor Plan Images

Gan

Wang

Huang

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…2015 [9] 122 FP LA -√ √ 2016 [10] 80 UP/FP LA/AT -√ -2017 [8] 870# FP LA -√ -2017 [11] 500 FP LA √ √ -2018 [5] 1.1K# FP LA -√ -2018 [6] 200 FP LA -√ -2018 [7] 115…”

Section: Datasetmentioning

confidence: 99%

SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing

Liu

Lai

Chen

et al. 2020

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Computer aided design (CAD) technology is widely used for architectural design, but current CAD tools still require high-level design specifications from human. It would be significant to construct an intelligent CAD system allowing automatic architectural layout parsing (AutoALP), which generates candidate designs or predicts architectural attributes without much user intervention. To tackle these problems, many learning-based methods were proposed, and benchmark dataset become one of the essential elements for the data-driven AutoALP. This paper proposes a new dataset called SCUT-AutoALP for multi-paradigm applications. It contains two subsets: 1) Subset-I is for floor plan design containing 300 residential floor plan images with layout, boundary and attribute labels; 2) Subset-II is for urban plan design containing 302 campus plan images with layout, boundary and attribute labels. We analyzed the samples and labels statistically, and evaluated SCUT-AutoALP for different layout parsing tasks of floor plan/urban plan based on conditional generative adversarial networks (cGAN) models. The results verify the effectiveness and indicate the potential applications of SCUT-AutoALP.

show abstract

“…In recent years, machine-learning techniques have been applied to detect the semantic classes (e.g., room, doors, and walls). For instance, de las Heras et al [7,8,38] presented a segmentation-based approach that merges the vectorization and identification of indoor elements into one procedure. Specifically, it first tiles the image of floor plans into small patches.…”

Section: Related Workmentioning

confidence: 99%

“…Currently, two mainstream indoor mapping methods include digitalization-based and measurement-based. The first provides digitized geometric maps comprising rooms, corridors, and doors extracted automatically from existing scanned maps [6][7][8][9]. Normally, it is incapable of extracting the room type information from the scanned map.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using Grammars to Infer Room Semantics

Fan

Noskov

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).

show abstract

CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool

Cited by 59 publications

References 38 publications

How Many Bedrooms Do You Need? A Real-Estate Recommender System from Architectural Floor Plan Images

How Many Bedrooms Do You Need? A Real-Estate Recommender System from Architectural Floor Plan Images

SCUT-AutoALP: A Diverse Benchmark Dataset for Automatic Architectural Layout Parsing

Feasibility of Using Grammars to Infer Room Semantics

Contact Info

Product

Resources

About