Parsing floor plan images

Abstract

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.