Laser scan data captures our physical world in large point clouds with data volumes often in the terabyte range. Processing this data is challenging in terms of both volume and extraction of usable information.
For the efficient processing of these gigantic point clouds, we use optimized spatial data structures in combination with special neural network architectures to segment objects and structures in the point clouds and use them for 3D reconstructions. This is a task that is especially important for point clouds of urban areas, but also very challenging. Such point clouds contain very heterogeneous and useful information, such as buildings, vegetation and infrastructure, but also non-stationary objects like pedestrians and vehicles. A successful example for the use of artificial intelligence is the reconstruction of sidewalks and platform edges from real world data using 3D CNNs (Convolutional Neural Networks), which we have already implemented in various projects with corporate partners.
