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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu


  • Ahti Kalervo
  • Juha Ylioinas
  • Markus Häikiö
  • Antti Karhu
  • Juho Kannala


  • CubiCasa Oy


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:


OtsikkoImage Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
ToimittajatMichael Felsberg, Per-Erik Forssén, Jonas Unger, Ida-Maria Sintorn
TilaJulkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaScandinavian Conference on Image Analysis - Norrköping, Ruotsi
Kesto: 11 kesäkuuta 201913 kesäkuuta 2019
Konferenssinumero: 21


NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta11482 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349


ConferenceScandinavian Conference on Image Analysis

ID: 35176950