Versailles-FP Dataset: Wall Detection in Ancient Floor Plans

Access to the floor plans of historical monuments over a time period is necessary in order to understand the architectural evolution and history. Such knowledge also helps to review (rebuild) the history by establishing connections between different events, persons and facts which were once part of the buildings. Since the two-dimensional plans do not capture the entire space, 3D modeling sheds new light on these unique archives and thus opens up great perspectives for understanding the ancient states of the monument. The first step towards generating the 3D model of the buildings and/or monuments is the wall detection inside the floor plan. Henceforth, the current work introduces a novel Versailles-FP dataset consisting Versailles Palace floor plan images and groundtruth in the form of wall masks regarding architectural developments during \(17^{th}\) and \(18^{th}\) century. The wall masks of the dataset are generated using an automated multi-directional steerable filters approach. The generated wall masks are then validated and corrected manually. We validate our approach of wall-mask generation in state-of-the-art modern datasets. Finally we propose a U-net based convolutional framework for wall detection. We have empirically shown that our U-net based method architecture achieves state-of-the-art results surpassing fully connected network based approach.