Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently
developed image classification approach. With origins in the computer vision and image …

Interest in semantic segmentation of 3D point clouds using ML and DL has grown due to
their key role in scene insight across a wide range of computer vision, robotics and remote …

Managing, protecting, and the evolutionary development of historical landscapes require
robust frameworks and processes for forming datasets and advanced decision support tools …

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 …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Point clouds are one of the most widely used data formats produced by depth sensors.
There is a lot of research into feature extraction from unordered and irregular point cloud …

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 …

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 …

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 …

Currently, as-built building information modeling (BIM) models from point clouds show great
potential in managing building information. The automatic creation of as-built BIM models …