On Valid and Invalid Three-Dimensional Geometries

Kazar, Baris M.; Kothuri, Ravi; Oosterom, Peter van; Ravada, Siva

doi:10.1007/978-3-540-72135-2_2

Cited by 37 publications

(42 citation statements)

References 20 publications

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“…Thus, sharing a single point is normally permitted (Figure 1), whereas sharing two or more points is not allowed (Figure 2). For 3D features, it is also makes sense to prohibit the connection of an inner ring with the outer ring in more than one point for most scenarios, because the volume of the solid would be no longer connected (Ledoux, 2013), (Kazar, Kothuri, van Oosterom, & Ravada, 2008). Consider the polygon in Figure 1 as the ground surface of a simple LoD1 building.…”

Section: Geometry Model Of Citygmlmentioning

confidence: 99%

Methods for Geometric Data Validation of 3d City Models

Wagner¹,

Alam²,

Wewetzer³

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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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Section: Geometry Model Of Citygmlmentioning

confidence: 99%

Methods for Geometric Data Validation of 3d City Models

Wagner¹,

Alam²,

Wewetzer³

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For example, Kazar et al describe methods for validating polygonal 3D geometries inside an Oracle database (Kazar et al, 2008). Nevertheless, this approach does not include healing capabilities.…”

Section: Related Workmentioning

confidence: 99%

Integrating Quality Management Into a 3d Geospatial Server

Coors

Krämer

2011

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In recent years the technology and workflow for producing and management of large 3D urban models has been established and widely been used. Standards such as CityGML enable the modelling and exchange of semantically enriched multi-purpose 3D urban models for applications like urban planning, public participation, environmental simulation and navigation. However, data quality management is essential to control and enhance the quality of these models in order to be able to meet the needs of the aforementioned applications. Quality management should be performed throughout the whole lifecycle of geospatial datasets-from data acquisition to processing, analysis and visualisation. In this paper, we therefore focus on the integration of a quality management software module into a 3D geospatial data server. First results of a prototype system developed at HFT Stuttgart together with Fraunhofer IGD will be presented in this paper as a starting point for further research into the field of quality management of 3D city models.

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“…Other issues like dealing with coplanarity, internal rings, constructing vertices, etc are equally important. These issues are explored in Kazar et al (2008), to test for Closedness and Connectedness in particular.…”

Section: Internal Validity Of 3d Parcelsmentioning

confidence: 99%

Data validation in 3D cadastre

Karki

Thompson

McDougall

2009

Developments in 3D Geo-Information Sciences

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In a cadastre, the 2D parcel is nowadays correctly considered to be a special case of the 3D parcel because the rights and restrictions extend beyond the surface itself. Storing, representing and manipulating a true 3D parcel however has not yet been satisfactorily achieved because of constraints in data modeling and software development. Significant research has been done to identify the best ways to represent a 3D solid, with rigorous mathematical testing on the respective merits of alternative approaches. Software companies have come up with their own ways of storing and validating 3D data, mostly as extensions of the 2D concepts. However, validation rules of one software may not be acceptable within another software"s validation environment. The validation itself can be specified in great detail but sometimes this leads to redundant, repetitive or unnecessary processing. Because of the high volume of data a typical organization may be expected to handle, it is necessary for the rules to be streamlined and efficient. In this paper, validation is initially approached to answer questions such as: what is validation? why it is necessary to validate?, and how do we validate?. Limiting the scope to the 3D geometry or spatial representation of a 3D cadastre, the paper takes a novel approach in identifying the various aspects of validation of a 3D cadastral parcel and identifies the critical validation factors. It examines the validity within individual parcels and the relationship between adjoining or overlapping parcels in 2D or 3D. Although it is difficult to ensure completeness of rules, critical validation rules are examined for each identified factor.2

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On Valid and Invalid Three-Dimensional Geometries

Cited by 37 publications

References 20 publications

Methods for Geometric Data Validation of 3d City Models

Methods for Geometric Data Validation of 3d City Models

Integrating Quality Management Into a 3d Geospatial Server

Data validation in 3D cadastre

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