  This paper presents a framework to address the challenges involved in
building point cloud cleaning, plane detection, and semantic segmentation, with
the ultimate goal of enhancing building modeling. We focus in the cleaning
stage on removing outliers from the acquired point cloud data by employing an
adaptive threshold technique based on z-score measure. Following the cleaning
process, we perform plane detection using the robust RANSAC paradigm. The goal
is to carry out multiple plane segmentations, and to classify segments into
distinct categories, such as floors, ceilings, and walls. The resulting
segments can generate accurate and detailed point clouds representing the
building's architectural elements. Moreover, we address the problem of semantic
segmentation, which plays a vital role in the identification and classification
of different components within the building, such as walls, windows, doors,
roofs, and objects. Inspired by the PointNet architecture, we propose a deep
learning architecture for efficient semantic segmentation in buildings. The
results demonstrate the effectiveness of the proposed framework in handling
building modeling tasks, paving the way for improved accuracy and efficiency in
the field of building modelization.
