  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.
