  In the reconstruction of fa\c{c}ade elements, the identification of specific
object types remains challenging and is often circumvented by rectangularity
assumptions or the use of bounding boxes. We propose a new approach for the
reconstruction of 3D fa\c{c}ade details. We combine MLS point clouds and a
pre-defined 3D model library using a BoW concept, which we augment by
incorporating semi-global features. We conduct experiments on the models
superimposed with random noise and on the TUM-FA\c{C}ADE dataset. Our method
demonstrates promising results, improving the conventional BoW approach. It
holds the potential to be utilized for more realistic facade reconstruction
without rectangularity assumptions, which can be used in applications such as
testing automated driving functions or estimating fa\c{c}ade solar potential.
