State of research in automatic as-built modelling

Pătrăucean, Viorica; Armeni, Iro; Nahangi, Mohammad; Yeung, Jamie; Brilakis, Ioannis; Haas, Carl T.

doi:10.1016/j.aei.2015.01.001

Cited by 278 publications

(105 citation statements)

References 119 publications

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“…As this requires manual work, some researchers also try to automate the process by using the LiDAR point cloud as an input for automated object creation. First results have been very promising, but it is still a challenge to extract objects and add semantic information to the extracted objects (Pătrăucean et al, 2015;Tatavarti, Papadakis, & Willis, 2017). This process is similar to "object-based image analysis" (OBIA) in two dimensions, but gets far more complex in three dimensions (Blaschke & Tomljenovic, 2017;Mayr & Rutzinger, 2018).…”

Section: Map Creation Processmentioning

confidence: 99%

Buildings in GI: How to deal with building models in the GIS domain

Knoth

Mittlboeck

Vockner

et al. 2019

Transactions in GIS

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Digital building information is important during a building's lifecycle, it is needed from first design until demolition. The two domains that mainly contribute are AEC (architecture, engineering, and construction) during design, construction, and operation, and GIS as a supporting discipline for analysis and further integration of the building's environment. However, there is a challenge in information exchange between the two domains, resulting in the remodeling of digital building information in GIS. In this article, we identify three major data sources from the AEC domain and show transformation processes to enable the integration of such models into the geographical environment in the form of one transition model. Furthermore, we show that this model can either be used directly or exported in the form of de facto standards that allow for further analysis.

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Section: Map Creation Processmentioning

confidence: 99%

Buildings in GI: How to deal with building models in the GIS domain

Knoth

Mittlboeck

Vockner

et al. 2019

Transactions in GIS

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“…A large body of literature has been devoted to generating surface-based primitives, especially planar surfaces (Pȃtrȃucean et al, 2015). Zhang et al (2015) present a sparsity-inducing optimization-based method to detect parametric planar patches and define their boundaries from noisy bridge point clouds.…”

Section: Bottom-up Detectionmentioning

confidence: 99%

Detection of Structural Components in Point Clouds of Existing RC Bridges

Brilakis

Middleton

2018

Computer aided Civil Eng

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105

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The cost and effort of modeling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule‐based classification to create labeled cuboids and cylinders from point clouds. Although these methods work well in synthetic data sets or idealized cases, they encounter huge challenges when dealing with real‐world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this article, we propose a novel top‐down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges oversegments into individually labeled point clusters. The results of 10 real‐world bridge point cloud experiments indicate that our method achieves very high detection performance. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labeled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labeled point clusters.

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“…A detailed survey of all segmentation methods can be found in Pătrăucean et al. (), Douillard et al. (), and Tang et al.…”

Section: Related Workmentioning

confidence: 99%