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.
@article{arxiv.1904.01920,
title = {CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis},
author = {Ahti Kalervo and Juha Ylioinas and Markus Häikiö and Antti Karhu and Juho Kannala},
journal= {arXiv preprint arXiv:1904.01920},
year = {2019}
}