Apartment Structure Estimation Using Fully Convolutional Networks and Graph Model

Abstract

Apartment searching has been a hot demand all over the world. Understanding the apartment structure will significantly contribute to simplifying the searching process. In this project, the fully convolutional networks are applied to generate semantic segmentation for the apartment floor plan images. We then study and optimize an algorithm to extract the graph model from the segmentation, and to extract the maximum common subgraph with which the structure similarity can be measured. We generate the ground truth data by using online annotation tool and experiment on half of the data. Our segmentation prediction results achieve a pixel mean accuracy of $0.89$ for the full-label model, and retrieve quite similar apartment structures by using graph modeling.