  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.
