Abstract
Abstract. A range of different and increasingly accessible acquisition methods, the possibility for frequent data updates of large areas, and a simple data structure are some of the reasons for the popularity of three-dimensional (3D) point cloud data. While there are multiple techniques for segmenting and classifying point clouds, capabilities of common data formats such as LAS for providing semantic information are mostly limited to assigning points to a certain category (classification). However, several fields of application, such as digital urban twins used for simulations and analyses, require more detailed semantic knowledge. This can be provided by semantic 3D city models containing hierarchically structured semantic and spatial information. Although semantic models are often reconstructed from point clouds, they are usually geometrically less accurate due to generalization processes. First, point cloud data structures / formats are discussed with respect to their semantic capabilities. Then, a new approach for integrating point clouds with semantic 3D city models is presented, consequently combining respective advantages of both data types. In addition to elaborate (and established) semantic concepts for several thematic areas, the new version 3.0 of the international Open Geospatial Consortium (OGC) standard CityGML also provides a PointCloud module. In this paper a scheme is shown, how CityGML 3.0 can be used to provide semantic structures for point clouds (directly or stored in a separate LAS file). Methods and metrics to automatically assign points to corresponding Level of Detail (LoD)2 or LoD3 models are presented. Subsequently, dataset examples implementing these concepts are provided for download.
Highlights
Three-dimensional point cloud data are increasingly relevant in the context of several applications such as digital urban and environmental twins, Building Information Modeling (BIM), autonomous driving, city modeling, and many others (Virtanen et al, 2017)
Point clouds are generally defined as a set of 3D points, each represented using X-/Y-/Z-coordinates and optionally accompanied with additional information on color (e.g. RGB values), intensity, or other attributes
While the file ”Points” contains four columns (X, Y, Z, and Intensity) the file ”Semantics” only provides one semantic label or class ID per corresponding point. This does not allow the representation of hierarchical structures but enables each point to be referenced to a specific object, rather than a classic label only indicating that points are of a certain category
Summary
Three-dimensional point cloud data are increasingly relevant in the context of several (emerging) applications such as digital urban and environmental twins, Building Information Modeling (BIM), autonomous driving, city modeling, and many others (Virtanen et al, 2017). Point clouds are often the foundation from which semantic models in different formats are derived. Processes such as ”Scan-to-BIM” still vastly rely on manual modelling, especially when it comes to object structuring and aggregation. Most semantically rich data formats do not support the integrated representation of point cloud geometries. The link between original point cloud data and resulting semantic models is mostly lost. Semantic models are often generalised and could benefit from geometrically more detailed information provided by corresponding point cloud data. Semantic interpretations of 3D point clouds are often done but mostly limited to classification. Point cloud data with additional semantic information would be beneficial for several domains (Poux, 2019). A new approach for bridging the gap between geometrically highly detailed point clouds and
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
