  Building Information Modeling (BIM) technology is a key component of modern
construction engineering and project management workflows. As-is BIM models
that represent the spatial reality of a project site can offer crucial
information to stakeholders for construction progress monitoring, error
checking, and building maintenance purposes. Geometric methods for
automatically converting raw scan data into BIM models (Scan-to-BIM) often fail
to make use of higher-level semantic information in the data. Whereas, semantic
segmentation methods only output labels at the point level without creating
object level models that is necessary for BIM. To address these issues, this
research proposes a hybrid semantic-geometric approach for clutter-resistant
floorplan generation from laser-scanned building point clouds. The input point
clouds are first pre-processed by normalizing the coordinate system and
removing outliers. Then, a semantic segmentation network based on PointNet++ is
used to label each point as ceiling, floor, wall, door, stair, and clutter. The
clutter points are removed whereas the wall, door, and stair points are used
for 2D floorplan generation. A region-growing segmentation algorithm paired
with geometric reasoning rules is applied to group the points together into
individual building elements. Finally, a 2-fold Random Sample Consensus
(RANSAC) algorithm is applied to parameterize the building elements into 2D
lines which are used to create the output floorplan. The proposed method is
evaluated using the metrics of precision, recall, Intersection-over-Union
(IOU), Betti error, and warping error.
