CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis
This work addresses the need for better tools in floorplan image analysis, particularly for AR/VR applications, by providing a novel dataset and method, though it is incremental in nature.
The paper tackles the lack of representative datasets for automatic floorplan image analysis by introducing CubiCasa5K, a large-scale dataset with 5000 samples annotated into over 80 categories, and presents an improved multi-task convolutional neural network method to boost research in this area.
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.