scholarly journals ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS

2016 ◽  
Vol III-3 ◽  
pp. 333-338 ◽  
Author(s):  
J. Meidow ◽  
H. Hammer ◽  
M. Pohl ◽  
D. Bulatov
Keyword(s):  
Laser Scanning ◽  
Initial Point ◽  
Real Data ◽  
Boundary Representation ◽  
Geometric Constraints ◽  
Data Set ◽  
3D City Models ◽  
Building Models ◽  
Recognition And Enforcement ◽  
City Models

Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The feasibility and the usability of the approach are demonstrated with a real data set.

scholarly journals ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS

2016 ◽  
Vol III-3 ◽  
pp. 333-338
Author(s):  
J. Meidow ◽  
H. Hammer ◽  
M. Pohl ◽  
D. Bulatov
Keyword(s):  
Laser Scanning ◽  
Initial Point ◽  
Real Data ◽  
Boundary Representation ◽  
Geometric Constraints ◽  
Data Set ◽  
3D City Models ◽  
Building Models ◽  
Recognition And Enforcement ◽  
City Models

Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The feasibility and the usability of the approach are demonstrated with a real data set.

scholarly journals UNLOCKING POINT CLOUD POTENTIAL: FUSING MLS POINT CLOUDS WITH SEMANTIC 3D BUILDING MODELS WHILE CONSIDERING UNCERTAINTY

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 45-52
Author(s):  
O. Wysocki ◽  
Y. Xu ◽  
U. Stilla
Keyword(s):  
Confidence Interval ◽  
Bayesian Network ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Dynamic Adjustment ◽  
3D City Models ◽  
Building Models ◽  
City Models

Abstract. Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of the captured 3D spatial phenomenon. Yet, each dataset has modality-dependent uncertainties that hinder their immediate fusion. Therefore, we present a method for fusing MLS point clouds with semantic 3D building models while considering uncertainty issues. Specifically, we show MLS point clouds coregistration with semantic 3D building models based on expert confidence in evaluated metadata quantified by confidence interval (CI). This step leads to the dynamic adjustment of the CI, which is used to delineate matching bounds for both datasets. Both coregistration and matching steps serve as priors for a Bayesian network (BayNet) that performs application-dependent identity estimation. The BayNet propagates uncertainties and beliefs throughout the process to estimate end probabilities for confirmed, unmodeled, and other city objects. We conducted promising preliminary experiments on urban MLS and CityGML datasets. Our strategy sets up a framework for the fusion of MLS point clouds and semantic 3D building models. This framework aids the challenging parallel usage of such datasets in applications such as façade refinement or change detection. To further support this process, we open-sourced our implementation.

3D City Modeling and Visualization for Smart Phone Applications

2012 ◽  
pp. 254-296
Author(s):  
Juha Hyyppä ◽  
Lingli Zhu ◽  
Zhengjun Liu ◽  
Harri Kaartinen ◽  
Anttoni Jaakkola
Keyword(s):  
Data Acquisition ◽  
Laser Scanning ◽  
3D Models ◽  
Image Capture ◽  
3D City Models ◽  
Central Computer ◽  
3D Data ◽  
Open Operation ◽  
City Modeling ◽  
City Models

Smartphones with larger screens, powerful processors, abundant memory, and an open operation system provide many possibilities for 3D city or photorealistic model applications. 3D city or photorealistic models can be used by the users to locate themselves in the 3D world, or they can be used as methods for visualizing the surrounding environment once a smartphone has already located the phone by other means, e.g. by using GNSS, and then to provide an interface in the form of a 3D model for the location-based services. In principle, 3D models can be also used for positioning purposes. For example, matching of images exported from the smartphone and then registering them in the existing 3D photorealistic world provides the position of the image capture. In that process, the central computer can do a similar image matching task when the users locate themselves interactively into the 3D world. As the benefits of 3D city models are obvious, this chapter demonstrates the technology used to provide photorealistic 3D city models and focus on 3D data acquisition and the methods available in 3D city modeling, and the development of 3D display technology for smartphone applications. Currently, global geoinformatic data providers, such as Google, Nokia (NAVTEQ), and TomTom (Tele Atlas), are expanding their products from 2D to 3D. This chapter is a presentation of a case study of 3D data acquisition, modeling and mapping, and visualization for a smartphone, including an example based on data collected by mobile laser scanning data from the Tapiola (Espoo, Finland) test field.

scholarly journals TOWARDS WIND-SIMULATION OF VIRTUAL 3D CITY MODELS IN A COLLABORATIVE VR ENVIRONMENT

2019 ◽  
Vol XLII-4/W15 ◽  
pp. 61-66 ◽  
Author(s):  
R. Piepereit ◽  
A. Beuster ◽  
M. von der Gruen ◽  
U. Voß ◽  
M. Pries ◽  
...  
Keyword(s):  
Flow Simulation ◽  
Level Of Detail ◽  
Planning Systems ◽  
3D City Models ◽  
Building Models ◽  
Wind Simulation ◽  
Development Processes ◽  
City Model ◽  
Simulation Results ◽  
City Models

<p><strong>Abstract.</strong> Virtual reality (VR) technologies are used more and more in product development processes and are upcoming in urban planning systems as well. They help to visualize big amounts of data in self-explanatory way and improve people’s interpretation of results. In this paper we demonstrate the process of visualizing a city model together with wind simulation results in a collaborative VR system. In order to make this kind of visualization possible a considerable amount of preliminary work is necessary: healing and simplification of building models, conversion of these data into an appropriate CAD-format and numerical simulation of wind flow around the buildings. The data obtained from these procedures are visualized in a collaborative VR-System. In our approach CityGML models in the LoD (Level of Detail) 1, 2 and 3 can be used as an input. They are converted into the STEP format, commonly used in CAD for simulation and representation. For this publication we use an exemplary LoD1 model from the district Stöckach-Stuttgart. After preprocessing the model, the results are combined with those of an air flow simulation and afterwards depicted in a VR system with a HTC Vive as well as in a CAVE and a Powerwall. This provides researchers, city planners and technicians with the means to flexibly and interactively exchange simulation results in a virtual environment.</p>

Automatic Generation of 3D Building Models with General Shaped Roofs for Sustainable Development

2013 ◽  
Vol 368-370 ◽  
pp. 1855-1859 ◽  
Author(s):  
Kenichi Sugihara ◽  
Zhen Jiang Shen
Keyword(s):  
Sustainable Development ◽  
Urban Planning ◽  
3D Modeling ◽  
Automatic Generation ◽  
Integrated System ◽  
Digital Maps ◽  
3D City Models ◽  
Building Models ◽  
Modeling Software ◽  
City Models

3D city models are important in urban planning for sustainable development. Usually and traditionally, urban planners design the future layout of the towns by drawing the maps, using GIS or CAD packages. 3D city models based on these maps are quite effective in understanding what if the plan is realized. However, creating 3D city models is labor intensive, using a 3D modeling software such as 3ds Max or SketchUp. In order to automate laborious steps, we are proposing a GIS and CG integrated system for automatically generating 3D building models with general shaped roofs by straight skeleton computation, based on general shaped building polygons (building footprints) on digital maps.

3D City Modeling and Visualization for Smart Phone Applications

2013 ◽  
pp. 1011-1052 ◽  
Author(s):  
Juha Hyyppä ◽  
Lingli Zhu ◽  
Zhengjun Liu ◽  
Harri Kaartinen ◽  
Anttoni Jaakkola
Keyword(s):  
Data Acquisition ◽  
Laser Scanning ◽  
3D Models ◽  
Image Capture ◽  
3D City Models ◽  
Central Computer ◽  
3D Data ◽  
Open Operation ◽  
City Modeling ◽  
City Models

Smartphones with larger screens, powerful processors, abundant memory, and an open operation system provide many possibilities for 3D city or photorealistic model applications. 3D city or photorealistic models can be used by the users to locate themselves in the 3D world, or they can be used as methods for visualizing the surrounding environment once a smartphone has already located the phone by other means, e.g. by using GNSS, and then to provide an interface in the form of a 3D model for the location-based services. In principle, 3D models can be also used for positioning purposes. For example, matching of images exported from the smartphone and then registering them in the existing 3D photorealistic world provides the position of the image capture. In that process, the central computer can do a similar image matching task when the users locate themselves interactively into the 3D world. As the benefits of 3D city models are obvious, this chapter demonstrates the technology used to provide photorealistic 3D city models and focus on 3D data acquisition and the methods available in 3D city modeling, and the development of 3D display technology for smartphone applications. Currently, global geoinformatic data providers, such as Google, Nokia (NAVTEQ), and TomTom (Tele Atlas), are expanding their products from 2D to 3D. This chapter is a presentation of a case study of 3D data acquisition, modeling and mapping, and visualization for a smartphone, including an example based on data collected by mobile laser scanning data from the Tapiola (Espoo, Finland) test field.

scholarly journals Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

2020 ◽  
Vol 12 (12) ◽  
pp. 1972 ◽  
Author(s):  
Urška Drešček ◽  
Mojca Kosmatin Fras ◽  
Jernej Tekavec ◽  
Anka Lisec
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Process Model ◽  
Laser Scanning ◽  
3D City Models ◽  
Aerial Vehicle ◽  
Spatial Etl ◽  
City Models

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

scholarly journals Modelling Buildings in CityGML LOD1: Building Parts, Terrain Intersection Curve and Address Features

2021 ◽  
Author(s):  
Evgeny Shirinyan ◽  
Dessislava Petrova-Antonova
Keyword(s):  
Multiple Sources ◽  
Intersection Curve ◽  
3D City Models ◽  
Building Model ◽  
3D City Model ◽  
Building Models ◽  
City Model ◽  
Open Datasets ◽  
Multiple Domains ◽  
City Models

3D city models integrate heterogeneous urban data from multiple sources in a unified geospatial representation, combining both semantics and geometry. Although in the last decades, they are predominantly used for visualization, today they are used in a large range of tasks related to exploration, analysis, and management across multiple domains. The complexity of urban processes and the diversity of urban environment bring challenges to the implementation of 3D city models. To address such challenges, this paper presents the development process of a 3D city model of a single neighborhood in Sofia city based on CityGML 2.0 standard. The model represents the buildings in LOD1 with a focus on CityGML features of related to the buildings like building part, terrain intersection curve and address. Similar building models of 18 cities provided as open datasets are explored and compared in order to extract good modeling practices. As a result, workflows for generation of 3D building models in LOD1 are elaborated and improvements in the feature modeling are proposed. Two options of building model are examined: modeling of a building as a single solid and modeling of a building with separate building parts. Finally, the possibilities for visualization of the model in popular platforms such as ArcGIS Pro and Cesium Ion are explored.

scholarly journals METHODS FOR GEOMETRIC DATA VALIDATION OF 3D CITY MODELS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 729-735 ◽  
Author(s):  
D. Wagner ◽  
N. Alam ◽  
M. Wewetzer ◽  
M. Pries ◽  
V. Coors
Keyword(s):  
Software Tool ◽  
Point Problem ◽  
Special Focus ◽  
Clear Understanding ◽  
Data Set ◽  
3D City Models ◽  
Starting Point ◽  
Corner Points ◽  
Geometric Quality ◽  
City Models

Geometric quality of 3D city models is crucial for data analysis and simulation tasks, which are part of modern applications of the data (e.g. potential heating energy consumption of city quarters, solar potential, etc.). Geometric quality in these contexts is however a different concept as it is for 2D maps. In the latter case, aspects such as positional or temporal accuracy and correctness represent typical quality metrics of the data. They are defined in ISO 19157 and should be mentioned as part of the metadata. 3D data has a far wider range of aspects which influence their quality, plus the idea of quality itself is application dependent. Thus, concepts for definition of quality are needed, including methods to validate these definitions. Quality on this sense means internal validation and detection of inconsistent or wrong geometry according to a predefined set of rules. A useful starting point would be to have correct geometry in accordance with ISO 19107. A valid solid should consist of planar faces which touch their neighbours exclusively in defined corner points and edges. No gaps between them are allowed, and the whole feature must be 2-manifold. In this paper, we present methods to validate common geometric requirements for building geometry. Different checks based on several algorithms have been implemented to validate a set of rules derived from the solid definition mentioned above (e.g. water tightness of the solid or planarity of its polygons), as they were developed for the software tool CityDoctor. The method of each check is specified, with a special focus on the discussion of tolerance values where they are necessary. The checks include polygon level checks to validate the correctness of each polygon, i.e. closeness of the bounding linear ring and planarity. On the solid level, which is only validated if the polygons have passed validation, correct polygon orientation is checked, after self-intersections outside of defined corner points and edges are detected, among additional criteria. Self-intersection might lead to different results, e.g. intersection points, lines or areas. Depending on the geometric constellation, they might represent gaps between bounding polygons of the solids, overlaps, or violations of the 2-manifoldness. Not least due to the floating point problem in digital numbers, tolerances must be considered in some algorithms, e.g. planarity and solid self-intersection. Effects of different tolerance values and their handling is discussed; recommendations for suitable values are given. The goal of the paper is to give a clear understanding of geometric validation in the context of 3D city models. This should also enable the data holder to get a better comprehension of the validation results and their consequences on the deployment fields of the validated data set.

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