  Plane model extraction from three-dimensional point clouds is a necessary
step in many different applications such as planar object reconstruction,
indoor mapping and indoor localization. Different RANdom SAmple Consensus
(RANSAC)-based methods have been proposed for this purpose in recent years. In
this study, we propose a novel method-based on RANSAC called Multiplane Model
Estimation, which can estimate multiple plane models simultaneously from a
noisy point cloud using the knowledge extracted from a scene (or an object) in
order to reconstruct it accurately. This method comprises two steps: first, it
clusters the data into planar faces that preserve some constraints defined by
knowledge related to the object (e.g., the angles between faces); and second,
the models of the planes are estimated based on these data using a novel
multi-constraint RANSAC. We performed experiments in the clustering and RANSAC
stages, which showed that the proposed method performed better than
state-of-the-art methods.
