Versailles-FP dataset: Wall Detection in Ancient
This work addresses the need for accurate 3D modeling of historical monuments to understand architectural evolution, though it is incremental as it builds on existing methods for wall detection.
The paper tackles the problem of wall detection in historical floor plans by introducing the Versailles-FP dataset, a collection of wall-groundtruthed images from the Versailles Palace, and achieves state-of-the-art results using a U-net based convolutional framework.
Access to historical monuments' floor plans over a time period is necessary to understand the architectural evolution and history. Such knowledge bases also helps to rebuild the history by establishing connection between different event, person and facts which are once part of the buildings. Since the two-dimensional plans do not capture the entire space, 3D modeling sheds new light on the reading of these unique archives and thus opens up great perspectives for understanding the ancient states of the monument. Since the first step in the building's or monument's 3D model is the wall detection in the floor plan, we introduce in this paper the new and unique Versailles FP dataset of wall groundtruthed images of the Versailles Palace dated between 17th and 18th century. The dataset's wall masks are generated using an automatic approach based on multi directional steerable filters. 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. Our method achieves state of the art result surpassing fully connected network based approach.