This article describes a complete unsupervised system for the segmentation of massive 3D
point clouds. Our system bridges the missing components that permit to go from 99 …

Large volumes of 3D parametric datasets, such as building information modeling (BIM), are
the foundation for develo** and applying smart city and digital twin technologies. Those …

Automation in point cloud data processing is central in knowledge discovery within decision-
making systems. The definition of relevant features is often key for segmentation and …

Reality capture allows for the reconstruction, with a high accuracy, of the physical reality of
cultural heritage sites. Obtained 3D models are often used for various applications such as …

The growing use of Building Information Model (BIM) is propelling an increase in demand for
creating or updating as-is models for existing tunnels. However, the geometric modeling can …

Developments in the field of artificial intelligence have made great strides in the field of
automatic semantic segmentation, both in the 2D (image) and 3D spaces. Within the context …

Point cloud data of indoor scenes is primarily composed of planar-dominant elements.
Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This …

Interpreting 3D point cloud data of the interior and exterior of buildings is essential for
automated navigation, interaction and 3D reconstruction. However, the direct exploitation of …

The AEC industry struggles with integrating close-range sensing data like photogrammetric
and LiDAR outputs due to the vast volume of unstructured 2D and 3D data, hindering tasks …

Discrete spatial datasets known as point clouds often lay the groundwork for decision-
making applications. Eg, we can use such data as a reference for autonomous cars and …