Creation of a 3-D City Model of Zurich with CC-Modeler (original) (raw)
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CC-Modeler: a topology generator for 3-D city models
ISPRS Journal of Photogrammetry and Remote Sensing, 1998
In this paper, we introduce a semi-automated topology generator for 3-D objects, CC-Modeler (CyberCity Modeler). Given the data as point clouds measured on Analytical Plotters or Digital Stations, we present a new method for fitting planar structures to the measured sets of point clouds. While this topology generator has been originally designed to model buildings, it can also be used for other objects, which may be approximated by polyhedron surfaces. We have used it so far for roads, rivers, parking lots, ships, etc. CC-Modeler is a generic topology generator. The problem of fitting planar faces to point clouds is treated as a Consistent Labeling problem, which is solved by probabilistic relaxation. Once the faces are defined and the related points are determined we apply a simultaneous least squares adjustment in order to fit the faces jointly to the given measurements in an optimal way. We first present the processing flow of the CC-Modeler. Then the algorithm of structuring the 3-D point data is outlined. Finally, we show the results of several data sets which have been produced with CC-Modeler.
CyberCity Modeler, a tool for interactive 3-D city model generation
Cybercity Modeler (CC-Modeler) is a semi-automated generator for 3-D objects of built-up environments. Given the primary data as point clouds measured on Analytical Plotters or Digital Stations, CC-Modeler presents a new method for fitting planar structures to the measured sets of point clouds. While this topology generator has been originally designed to model buildings, it can also be used for other objects, which may be approximated by polyhedron surfaces. We have used it so far for roads, rivers, parking lots, ships, etc. CC-Modeler is a generic topology generator. The problem of fitting planar faces to point clouds is treated as a Consistent Labeling problem, which is solved by probabilistic relaxation. Once the faces are defined and the related points are determined, a simultaneous least squares adjustment is applied in order to fit the faces jointly to the given measurements in an optimal way. We first present the processing flow of CC-Modeler. Then, the algorithm of structuring the 3-D point data is outlined. Finally, we show the results of several data sets that have been produced with CC-Modeler. Also, reference is made to a new concept for a spatial information system (CC-SIS, CyberCity Spatial Information System)which is currently under development.
Towards fully automated 3D city model generation
Three-dimensional city models are usually comprised of a description of the terrain, streets, buildings and vegetation in build-up areas. Building models are an important part thereof, even though it has to be noted that for many applications, additional information is necessary. For example, a faithful representation for virtual reality applications can only be obtained when the texture of the ground, roofs and façades is present and important details like trees, walkways and fences are present .
Building Facade Modeling From 3D Urban Point Clouds
Master Thesis, 2019
A 3D city model represents the earth surface and objects related to urban areas such as building models, street furniture, and vegetation. As a component of the 3D city model, a building model consists of several sub-components, namely building footprint, building height, roof structure, building facade, and interior structure. The last two sub-components are necessary for a 3D city model to impart a high level of detail. There are many data sources to model a building facade. One of the most popular is urban 3D point cloud, which is acquired by laser scanning or photogrammetry. However, extracting building facade information from 3D point cloud followed by constructing the facade geometric model is still a challenging task due to the enormous data volume, high complexity, shape regularities, computational efficiency, and data diversity. As a solution, this thesis presents a complete workflow to construct building facade model from urban 3D point clouds. The workflow provides a solution to segment the facade points from urban 3D point clouds, to find primitive geometries on the building facade points, to extract and regularize the features of the primitive geometries, to geometrically model the building facade, and to visualize the generated building facade model. The building facade points are extracted from the 3D point clouds using geometrical and morphological operations. All the operations are performed on the pixel-based implementation. Thus, it is efficient, flexible, and simple to implement; even the data is unstructured and comprises a large number of points. In the following step, the appearances of plane geometries are searched over the building facade points. A combination of eigen-analysis, normal distribution transformation, and voxel growing techniques are implemented for this purpose. This method successfully takes advantages of these three techniques while reducing drawbacks. It also succeeds in detecting the planes locally, avoiding the weakness of the global plane segmentation approaches. The idea of the improved slicing method is then adapted to detect the boundary points and the openings. It is modified during the implementation so that it works effectively and can keep topological information of the plane, bounded objects, and the boundary points. This information is beneficial for the rest of the stages. Another task is the boundary line detection, which is performed using 2D Hough transform algorithm. Spurious detected lines as the effect of the global approach can be avoided by taking advantage of the above-mentioned topological information. The detected lines are then used to regularize the bounded shapes and to generate the corner points. The produced line segments and corner points are represented as graphs so that the shape errors can be easily detected and refined. In the next step, the properties of the cyclic graph are explored to geometrically model the bounded shapes as inner and outer polygons. The topology information between inner and outer polygons are then modeled to get the complete polygonal face representation. For visualization, these polygonal faces are printed into a Well-Known Text format. The proposed approach is implemented on two different datasets. The implementation processes and the obtained results are thoroughly elaborated. A comprehensive evaluation is conducted in the next chapter, while the conclusions and possible outlooks are discussed in the final chapter.
Increasing Significance of 3D Topology for Modelling of Urban Structures
Demand driven growth of construction activities in the rapidly expanding urban areas has become a global phenomenon. With the advancement of technologies, expectations are increased where valid 3D volumes can be calculated with least errors. Can 3D topology and topological data structures help in achieving better accuracy and facilitate the maintenance of 3D models? Complex constructions get immense support if the data structures are 3D compatible and thus can be visualized in a 3D environment. It is important to have accurate alignments of the adjoining objects in 3 dimensions since errors will not only affect the horizontally adjacent objects but also the objects on the surface below or above it. The paper aims to highlight the significance of 3D topology for the applications where we need to work with spatial datasets of above and under surface. We illustrate with a small example that current practice of creating 3D models is insufficient to validate their objects consistency.
Automatic 3D Buildings Compact Reconstruction from Lidar Point Clouds
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids).
AUTOMATIC MATCHING OF TERRESTRIAL SCAN DATA AS A BASIS FOR THE GENERATION OF DETAILED 3D CITY MODELS
The request for three-dimensional digital city models is increasing and also the need to have more precise and realistic models. In the past, 3D models have been relatively simple. The models were derived from aerial images or laser scanning data and the extracted buildings were represented by simple shapes. However, for some applications, like navigation with landmarks or virtual city tours, the level of details of such models is not high enough. The user demands more detailed and realistic models. Nowadays, the generation of detailed city models includes usually a large amount of manual work, since single buildings are often reconstructed using CAD software packages and the texture of facades is mapped manually to the building primitives. Using terrestrial laser scanners, accurate and dense 3D point clouds can be obtained. This data can be used to generate detailed 3D-models, which also include facade structures. Since the technology of laser scanning in the field of terrestrial data acquisition for surveying purposes is new, the processing of the data is only poorly conceived. This paper makes a contribution to the automatic registration of terrestrial laser scanning data recorded from different viewpoints. Up to now, vendors of laser scanners mainly use manual registration mechanisms combined with artificial targets such as retro-reflectors or balls to register single scans. Since these methods are not fully automated, the registration of different scans is time consuming. Furthermore, the targets must be placed sensibly within the scan volume, and often require extra detail scans of the targets in order to achieve accurate transformation parameters. In this paper it is shown how to register different scans using only the measured point clouds themselves without the use of special targets in the surveyed area.
An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures
Journal of Geographical Research, 2022
This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also, making and forming 3D models of buildings. Models and tools for creating tools made in the model builder application within the ArcGIS Pro software. An unclassified point cloud obtained by the LiDAR system was used for the model input data. The point cloud, collected by the airborne laser scanning system (ALS), is classified into several classes: ground, high and low noise, and buildings. Based on the created DEMs, points classified as buildings and formed prints of buildings, realistic 3D city models were created. Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.
http://www.cgv.tugraz.at/cityfit ABSTRACT: Many approaches for automatic 3D city reconstruction exist, but they are still missing an important feature: detailed facades. The goal of the CityFit project is to reconstruct the facades of 80% of the buildings in the city of Graz fully automatically. The challenge is to establish a complete workflow, ranging from acquisition of images and LIDAR data over 2D/3D feature detection and recognition to the generation of lean polygonal facade models. The desired detail level is to represent all significant facade elements larger than 50 cm by explicit polygonal geometry. All geometry shall also carry descriptive annotations (semantic enrichment). This paper presents an outline of the workflow, important design decisions, and the current state of the project. First results were obtained by case studies of facade analysis followed by manual reconstruction. This gave important hints how to structure grammars for automatic reconstruction.