In the cultural heritage field, point clouds, as important raw data of geomatics, are not only
three-dimensional (3D) spatial presentations of 3D objects but they also have the potential …

The manual creation of digital models of existing buildings for operations and maintenance
is difficult and time-consuming. Machine learning and deep learning techniques have …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

This work presents a semi-automatic approach to the 3D reconstruction of Heritage-Building
Information Models from point clouds based on machine learning techniques. The use of …

In recent years semantic segmentation of 3D point clouds has been an argument that
involves different fields of application. Cultural heritage scenarios have become the subject …

Building information modelling (BIM) is evolving significantly in the architecture, engineering
and construction industries. BIM involves various remote-sensing tools, procedures and …

In recent years, deep learning techniques for processing 3D point cloud data have seen
significant advancements, given their unique ability to extract relevant features and handle …

Pattern recognition (PR) is the study of how machines can examine the environment, learn
to distinguish patterns of interest from their background, and make reliable and feasible …

The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is
hampering the development of automatic classification solutions in this field. Heritage 3D …

The recent years saw an extensive use of 3D point cloud data for heritage documentation,
valorisation and visualisation. Although rich in metric quality, these 3D data lack structured …