3D Building Detection and Modeling from Aerial LIDAR Data

Verma, Vishal; Kumar, Raman; Hsu, Sheng Teng

doi:10.1109/cvpr.2006.12

Cited by 238 publications

(195 citation statements)

References 3 publications

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“…Adjacency graphs over planes have been used for 3D roof detection from aerial LIDAR data, see e.g. [14]. In [15], Schnabel et al present a representation of adjacency graphs over primitives that is similar to ours.…”

Section: Related Workmentioning

confidence: 90%

Querying 3D Data by Adjacency Graphs

Bore

Folkesson

2015

Lecture Notes in Computer Science

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Abstract. The need for robots to search the 3D data they have saved is becoming more apparent. We present an approach for finding structures in 3D models such as those built by robots of their environment. The method extracts geometric primitives from point cloud data. An attributed graph over these primitives forms our representation of the surface structures. Recurring substructures are found with frequent graph mining techniques. We investigate if a model invariant to changes in size and reflection using only the geometric information of and between primitives can be discriminative enough for practical use. Experiments confirm that it can be used to support queries of 3D models.

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Section: Related Workmentioning

confidence: 90%

Querying 3D Data by Adjacency Graphs

Bore

Folkesson

2015

Lecture Notes in Computer Science

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“…(Habib et al, 2010;Kedzierski and Fryskowska, 2014;Xiong et al, 2015)), we have examined several categories of sources of data. For instance, national standards and guidelines (AdV, 2011(AdV, , 2013Stoter et al, 2014;Blaauboer et al, 2013; Chinese Ministry of Housing and Urban-Rural Development, 2010), examples of series and specifications of 3D city models not related to CityGML (Köninger and Bartel, 1998;Batty et al, 2000;Kemec et al, 2012;Schilcher et al, 1998;Thiemann, 2003;Demir and Baltsavias, 2012), usually in the field of 3D generalisation (Zhao et al, 2012;Li et al, 2013), visualisation (Andujar et al, 2010;Rau and Cheng, 2013) and 3D reconstruction (Huang et al, 2011;Verma et al, 2006).…”

Section: Methodsmentioning

confidence: 99%

An improved LOD specification for 3D building models

Biljecki

Ledoux

Stoter

2016

Computers, Environment and Urban Systems

350

176

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The level of detail (LOD) concept of the OGC standard CityGML 2.0 is intended to differentiate multi-scale representations of semantic 3D city models. The concept is in practice principally used to indicate the geometric detail of a model, primarily of buildings. Despite the popularity and the general acceptance of this categorisation, we argue in this paper that from a geometric point of view the five LODs are insufficient and that their specification is ambiguous.We solve these shortcomings with a better definition of LODs and their refinement. Hereby we present a refined set of 16 LODs focused on the grade of the exterior geometry of buildings, which provide a stricter specification and allow less modelling freedom. This series is a result of an exhaustive research into currently available 3D city models, production workflows, and capabilities of acquisition techniques. Our specification also includes two hybrid models that reflect common acquisition practices. The new LODs are in line with the LODs of CityGML 2.0, and are intended to supplement, rather than replace the geometric part of the current specification. While in our paper we focus on the geometric aspect of the models, our specification is compatible with different levels of semantic granularity. Furthermore, the improved LODs can be considered format-agnostic.Among other benefits, the refined specification could be useful for companies for a better definition of their product portfolios, and for researchers to specify data requirements when presenting use cases of 3D city models. We support our refined LODs with experiments, proving their uniqueness by showing that each yields a different result in a 3D spatial operation.Keywords: Level of detail; 3D city modelling; 3D GIS; 3D building models; CityGML; Scale Highlights• CityGML LODs are an industry standard for conveying the grade of 3D city models.• The 5 LODs are not defined precisely, and they are not sufficient for this purpose.• We present a refined series of 16 LODs that overcomes these issues.

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“…In order to relate specific features extracted from the data to general knowledge about building shapes a library of elementary building structures is established. Roof structure recognition is performed using topology graphs, which describe the topological relation between roof planes (Verma et al, 2006). Building graph pieces are unambiguously matched with simple sub-graphs associated with basic elements from the library.…”

Section: Building Modelingmentioning

confidence: 99%

Quality Analysis on 3d Buidling Models Reconstructed From Uav Imagery

Jarzabek-Rychard¹,

Karpina²

2016

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

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ABSTRACT:Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.

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3D Building Detection and Modeling from Aerial LIDAR Data

Cited by 238 publications

References 3 publications

Querying 3D Data by Adjacency Graphs

Querying 3D Data by Adjacency Graphs

An improved LOD specification for 3D building models

Quality Analysis on 3d Buidling Models Reconstructed From Uav Imagery

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