Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model

Xu, Yangsheng; Tuttas, Sebastian; Hoegner, L.; Stilla, Uwe

doi:10.1016/j.patrec.2017.12.016

Cited by 63 publications

(30 citation statements)

References 8 publications

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“…Hence, these methods tend to perform well in relatively simplified scenarios and with synthetic data, but are not ready to tackle the real bridge components whose asconstructed and as-weathered shapes further increase the as-designed complexity. To reduce computational time, Xu et al (2018) suggest an octree-based probabilistic segmentation model for construction sites. The authors partitioned the scene into voxels.…”

Section: Bottom-up Detectionmentioning

confidence: 99%

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Detection of Structural Components in Point Clouds of Existing RC Bridges

Brilakis

Middleton

2018

Computer aided Civil Eng

108

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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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Section: Bottom-up Detectionmentioning

confidence: 99%

“…To reduce computational time, Xu et al. () suggest an octree‐based probabilistic segmentation model for construction sites. The authors partitioned the scene into voxels.…”

Section: Introductionmentioning

confidence: 99%

Detection of Structural Components in Point Clouds of Existing RC Bridges

Brilakis

Middleton

2018

Computer aided Civil Eng

108

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“…Truong-Hong et al (2013) introduced an OB-based technique to automatically extract building façade features in point clouds. Xu et al (2018) suggested an OB probabilistic segmentation model for construction sites. However, the segmentation accuracy of this method is quite sensitive to the voxel size.…”

Section: Bottom-up Detectionmentioning

confidence: 99%

Generating bridge geometric digital twins from point clouds

Lu¹,

Brilakis²

2019

Computing in Construction

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The automation of digital twinning for existing bridges from point clouds remains unresolved. Previous research yielded methods that can generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with real-world point clouds. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. Experiments on ten bridge point clouds indicate the framework can achieve high and reliable performance of geometric digital twin generation of existing bridges. End-user requirements (EURs)Developing detailed EURs of DTs is outside the scope of this study. This section summarizes the fundamental information that a DT must contain. The end-users of DTs are inspectors, engineers, and the decision makers. The EURs define the information that will be required by the end-users from both their own internal team and from suppliers. The EURs should clearly articulate the information requirements and describe the expected information deliverables. However, the nature of the EURs depends on the complexity of the project, the experience, and the requirements of the end-users. Experienced end-users may develop detailed EURs, whilst others may only set out highlevel requirements, and basic rules. Broadly, a DT includes:EUR 1: Component-level digital representation.

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“…Graph-based clustering aims to divide a dataset into disjoint subsets with members similar to each other from the affinity matrix. In (Xu et al, 2018b), the use of the local graph structure for the description of the 3D geometry with the supervoxel structure has been tested. The use of the local graph model can make the clustering process quite efficient and available for parallel computing when combined with regiongrowing strategy.…”

Section: Global Graph-based Clusteringmentioning

confidence: 99%

“…Once the global graph of all the supervoxels is constructed, we can optimize the connection of each supervoxel by clustering nodes of the constructed global graph. To this end, similarly to work in (Xu et al, 2018b), we resolve the graph clustering problem via the adaption of the efficient graph-based segmentation method proposed in (Felzenszwalb, Huttenlocher, 2004). After the connections of all the voxels are identified, the connected voxels are clustered into one segment.…”

Section: Global Graph-based Clusteringmentioning

confidence: 99%

Contour Extraction of Planar Elements of Building Facades From Point Clouds Using Global Graph-Based Clustering

Stilla

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In this work, we present a surface-based method to extract the contours of planar building elements in the urban scene. A bottom-up segmentation method that utilizes global graph-based optimization and supervoxel structure is developed, enabling an automatic and unsupervised segmentation of point clouds. Then, a planarity-based extraction is conducted to segments, and only the planar segments, as well as their neighborhoods, are selected as candidates for the plane fitting. The points of the plane can be identified by the parametric model given by the planarity calculation. Afterward, the boundary points of the extracted plane are extracted by the alpha-shape. Optionally, line segments can be fitted and optimized by the energy minimization with the local graphical model. The experimental results using different datasets reveal that our proposed segmentation methods can be effective and comparable with other method, and the contours of planar building elements can be well extracted from the complex urban scene.</p>

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Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model

Cited by 63 publications

References 8 publications

Detection of Structural Components in Point Clouds of Existing RC Bridges

Detection of Structural Components in Point Clouds of Existing RC Bridges

Generating bridge geometric digital twins from point clouds

Contour Extraction of Planar Elements of Building Facades From Point Clouds Using Global Graph-Based Clustering

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