CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

CubiCasa5K : A Dataset and an Improved Multi-task Model for Floorplan Image Analysis. / Kalervo, Ahti; Ylioinas, Juha; Häikiö, Markus; Karhu, Antti; Kannala, Juho.

Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. toim. / Michael Felsberg; Per-Erik Forssén; Jonas Unger; Ida-Maria Sintorn. 2019. s. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11482 LNCS).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Kalervo, A, Ylioinas, J, Häikiö, M, Karhu, A & Kannala, J 2019, CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis. julkaisussa M Felsberg, P-E Forssén, J Unger & I-M Sintorn (toim), Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vuosikerta. 11482 LNCS, Sivut 28-40, Norrköping, Ruotsi, 11/06/2019. https://doi.org/10.1007/978-3-030-20205-7_3

APA

Kalervo, A., Ylioinas, J., Häikiö, M., Karhu, A., & Kannala, J. (2019). CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis. teoksessa M. Felsberg, P-E. Forssén, J. Unger, & I-M. Sintorn (Toimittajat), Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings (Sivut 28-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11482 LNCS). https://doi.org/10.1007/978-3-030-20205-7_3

Vancouver

Kalervo A, Ylioinas J, Häikiö M, Karhu A, Kannala J. CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis. julkaisussa Felsberg M, Forssén P-E, Unger J, Sintorn I-M, toimittajat, Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. 2019. s. 28-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-20205-7_3

Author

Kalervo, Ahti ; Ylioinas, Juha ; Häikiö, Markus ; Karhu, Antti ; Kannala, Juho. / CubiCasa5K : A Dataset and an Improved Multi-task Model for Floorplan Image Analysis. Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings. Toimittaja / Michael Felsberg ; Per-Erik Forssén ; Jonas Unger ; Ida-Maria Sintorn. 2019. Sivut 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex - Lataa

@inproceedings{7f2faf1c1da343e28e375b9e8deba2f4,
title = "CubiCasa5K: A Dataset and an Improved Multi-task Model for Floorplan Image Analysis",
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.",
keywords = "Convolutional neural networks, Dataset, Floorplan images, Multi-task learning",
author = "Ahti Kalervo and Juha Ylioinas and Markus H{\"a}iki{\"o} and Antti Karhu and Juho Kannala",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-20205-7_3",
language = "English",
isbn = "9783030202040",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "28--40",
editor = "Michael Felsberg and Per-Erik Forss{\'e}n and Jonas Unger and Ida-Maria Sintorn",
booktitle = "Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings",

}

RIS - Lataa

TY - GEN

T1 - CubiCasa5K

T2 - A Dataset and an Improved Multi-task Model for Floorplan Image Analysis

AU - Kalervo, Ahti

AU - Ylioinas, Juha

AU - Häikiö, Markus

AU - Karhu, Antti

AU - Kannala, Juho

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Convolutional neural networks

KW - Dataset

KW - Floorplan images

KW - Multi-task learning

UR - http://www.scopus.com/inward/record.url?scp=85066882608&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-20205-7_3

DO - 10.1007/978-3-030-20205-7_3

M3 - Conference contribution

SN - 9783030202040

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 28

EP - 40

BT - Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings

A2 - Felsberg, Michael

A2 - Forssén, Per-Erik

A2 - Unger, Jonas

A2 - Sintorn, Ida-Maria

ER -

ID: 35176950