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Title Automatic Floor Plan Analysis and Recognition
Authors Pablo Pizarro, Nancy Hitschfeld, Iván Sipirán, Jose Saavedra
Publication date June 2022
Abstract Due to recent advances in machine learning, there has been
explosive development of multiple methodologies
that automatically extract information from architectural floor plans.
Nevertheless, the lack of a standard no
tation and the high variability in style and composition make it urgent to
devise reliable and effective approaches
to analyze and recognize objects like walls, doors, and rooms from
rasterized images. For such reason, and with
the aim of bringing some significant contribution to the state-of-the-art,
this paper provides a critical revision of
the methodologies and tools from rule-based and learning-based approaches
between the years 1995 to 2021.
Datasets, scopes, and algorithms were discussed to guide future developers
to improve productivity and reduce
costs in the construction and design industries. This study concludes that
most research relies on a particular plan
style, facing problems regarding generalization and comparison due to the
lack of a standard metric and the
limited public datasets. However, the study also highlights that combining
existing tasks can be employed in
various and increasing applications.
Pages article 104348
Volume 140
Reference URL View reference page