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.
Parallel to @cite_11 , a CNN-based method for parsing floorplan images using segmentation, object detection, and character recognition was proposed in @cite_14 . The main difference to @cite_11 is that the given tasks are all performed using isolated networks. The experiments performed in @cite_14 on wall segmentation clearly demonstrated the superiority of a CNN-based approach compared with some traditional patch-based models utilizing standard shallow classifiers like support vector machines. In summary, the era of deep neural networks has given rise to significantly better methods for 2D floorplan analysis. According to @cite_11 @cite_14 , especially fully convolutional CNNs have a huge potential in extracting accurate pixel-level geometric and semantic information that can be further utilized in later post-processing steps to construct more effective heuristics to restore the lost floorplan elements.
@cite_14 @cite_11
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This paper introduces a method for analyzing floor plan images using wall segmentation, object detection, and optical character recognition. We introduce a challenging new real-estate floor plan dataset, R-FP, evaluate different wall segmentation methods, and propose fully convolutional networks (FCN) for this task. We explore architectures with different pixel-stride values and more compact ones with skipped pooling layers. An FCN-2s with a 2-pixel stride layer achieves state-of-the-art performance, obtaining a mean Intersection-over-Union score of 89.9 on R-FP, and 94.4 on the public CVC-FP data set. Using OCR and object detection, we estimate room sizes. Finally, we show applications in automatic 3D model building and interactive furniture fitting. This paper addresses the problem of converting a rasterized floorplan image into a vector-graphics representation. Unlike existing approaches that rely on a sequence of lowlevel image processing heuristics, we adopt a learning-based approach. A neural architecture first transforms a rasterized image to a set of junctions that represent low-level geometric and semantic information (e.g., wall corners or door end-points). Integer programming is then formulated to aggregate junctions into a set of simple primitives (e.g., wall lines, door lines, or icon boxes) to produce a vectorized floorplan, while ensuring a topologically and geometrically consistent result. Our algorithm significantly outperforms existing methods and achieves around 90 precision and recall, getting to the range of production-ready performance. The vector representation allows 3D model popup for better indoor scene visualization, direct model manipulation for architectural remodeling, and further computational applications such as data analysis. Our system is efficient: we have converted hundred thousand production-level floorplan images into the vector representation and generated 3D popup models.