Human Identification Using Automotive Radar

In this study, we perform human identification using accumulated radar point clouds in an outdoor scene. We employ PointNet as classification network and explore the impact of adding radars' non-spatial features as input, namely doppler velocity and radar cross section (RCS). Furthermore, we encode time as an additional time identity dimension...

Analyzing deformation of buildings using LiDAR point clouds obtained by a Mobile Laser Scanning System

Unequal deformation of the soil can cause deformation or damage to buildings, like tilted facades or cracks in walls. This research investigates how deformation of a building can be analyzed using Light Detection And Ranging (LiDAR) data. Cyclomedia captures LiDAR data yearly in the Netherlands making it possible to analyze either one or...

3D Road Boundary Mapping of MLS Point Clouds

Roads in modern cities facilitate different types of users, including car drivers, cyclists, and pedestrians. These different users often have a designated section of the road to operate on. Road management, e.g., by municipalities, needs to take this sectioning into account, preferably in an efficient way. Mobile laser scanning (MLS) point...

Semantic Segmentation of Large-scale Urban Scenes from Point Clouds

Deep learning methods have been demonstrated to be promising in semantic segmentation of point clouds. Existing works focus on extracting informative local features based on individual points and their local neighborhood. They lack consideration of the general structures and latent contextual relations of underlying shapes among points. To this...

Semi-automatic fold detection in geological outcrops using Laser Scanning data: Applied to a limestone-marl sequences in the Vocontian Basin, France

Geological surveying is a common practice performed by geologists nowadays. Determining layer orientation parameters and identifying folds are examples of features needed in order to create a geological map or model of the (sub) surface. Surveying is expensive and time consuming, therefore such survey is recently being investigated and...

Classification of large scale outdoor point clouds using convolutional neural networks

There is a paradigm shift from two- to three-dimensional data, from maps to information dense models. Self-driving cars, digitization of historic buildings or maintenance of highway infrastructure are a small selection of many applications that use laser scanning to acquire three-dimensional data of our physical surroundings. Most of these...

Chronocity: Technical Report Towards an Open Point Cloud Map supporting on-the-fly change detection

We are now gradually entering the era of big data - maybe a bit too much of a buzzword, but it is not lied. Technology is evolving fast, enabling faster and more efficient data acquisition, storage, retrieval and processing. Point cloud datasets are such a type which relies on large files and lots of processing power. The rather fast evolutions...