Integration of 3d Point Clouds With Semantic 3d City Models – Providing Semantic Information Beyond Classification

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.

Tum-Façade: Reviewing and Enriching Point Cloud Benchmarks for Façade Segmentation

“…This enables modeling of CityGML models at LoD3 (Gröger et al, 2012), or at so-called hybrid LoD with a fac ¸ade at LoD3 and a roof structure at LoD2 (Biljecki et al, 2016). Moreover, this feature can also be used for linking the segmented point clouds to existing building models without explicit reconstruction, as demonstrated by (Beil et al, 2021).…”

“…Another challenge concerns single-object semantics, typically derived in the semantic segmentation process. Such an approach implicitly discards the hierarchical complexity of the semantic data model, rendering it insufficient for directly creating semantic 3D data models, for instance, embedding 3D window objects into a wall surface belonging to a building entity within a city model [4].…”

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

“…Semantically segmented point clouds are the foundation for creating 3D city models. The resulting semantic models are used to create DTCs that support a plethora of urban applications [5].…”

3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data

“…Such models should not only enable rapid visualization of 3D datasets at different scales and with varying complexity, but they should also facilitate data processing during comprehensive analysis [19,21]. In the next stage of designing a smart city, 3D datasets describing individual buildings must be linked with semantic data [22] to create thematic applications [23]. The CityGML 3.0 Transportation Model also requires highly detailed models of street spaces, in particular buildings.…”

“…The CityGML 3.0 Transportation Model also requires highly detailed models of street spaces, in particular buildings. These models are utilized in autonomous vehicles [22,24] and other mobile mapping systems.…”