Semantic segmentation of point clouds with the 3D medial axis transform

A point cloud is a representation of shapes, organized in a 3D irregular structure. Point clouds are increasingly used in different applications, ranging from architectural preservation to computer vision. The 3D medial axis transform is a topology preserving, skeleton representation of shapes. It can be used to decompose an object in meaningful...

Visibility Analysis in a Point Cloud based on the Medial Axis Transform

This thesis proposes a novel medial axis transform (MAT) based method to achieve visibility analysis in a point cloud. There are several advantages of this MAT based method. This method avoids surface reconstruction from a point cloud. It also works for the situation when there is surface missing in the input point cloud. For different point...

3D breakline extraction from point clouds with the Medial Axis Transform

This project develop a new method to generate breakline from the point cloud directly with the MAT. The breakline is a structured line of the object surface which has high curvature. Meanwhile, the reciprocal of the medial edge ball's radius can represent the point with high curvature, which is the fundamental idea of this project. The key parts...

Geographical point cloud modelling with the 3D medial axis transform

A geographical point cloud is a detailed three-dimensional representation of the geometry of our geographic environment. Using geographical point cloud modelling, we are able to extract valuable information from geographical point clouds that can be used for applications in asset management, crisis management, city and landscape planning, and...

Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud

Drainage networks play a crucial role in protecting land against floods. It is therefore important to have an accurate map of the watercourses that form the drainage network. Previous work on the automatic identification of watercourses was typically based on grids, focused on natural landscapes, and used mostly the slope and curvature of the...

Robust approximation of the Medial Axis Transform of LiDAR point clouds as a tool for visualisation

Governments and companies around the world collect point clouds (datasets containing elevation points) because these are useful for many applications, e.g. to reconstruct 3D city models, to understand and predict the impact of floods, and to monitor dikes. We address in this paper the visualisation of point clouds, which is perhaps the most...

Creating the medial axis transform for billions of lidar points using a memory efficient method

Using Light Detection And Radar (LiDAR) large parts of the earth's geography can be captured an represented as a 3D pointcloud. The whole elevation dataset of the Netherlands (AHN2) is currently available and captured using this technique, it contains around 640 billion points. These massive dataset in its current form is well suited for...