A self-adaptive segmentation method for a point cloud

Fan, Yuling; Wang, Meili; Geng, Nan; He, Dongjian; Chang, Jian; Zhang, Jian Jun

doi:10.1007/s00371-017-1405-6

Cited by 27 publications

(17 citation statements)

References 21 publications

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“…Subsequently, the point cloud is segmented. Typically, lines are used for point cloud segmentation in 2D methods such as RANSAC based [8,11,12] and Hough transform based [13][14][15] methods, while planes are used for point cloud segmentation in 3D methods [10,16,17]. Finally, the segments are classified into categories such as floors and walls by using heuristics or machine learning techniques [18][19][20][21].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Automatic 2D Floorplan CAD Generation from 3D Point Clouds

Gankhuyag

Han

2020

Applied Sciences

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In the architecture, engineering, and construction (AEC) industry, creating an indoor model of existing buildings has been a challenging task since the introduction of building information modeling (BIM). Because the process of BIM is primarily manual and implies a high possibility of error, the automated creation of indoor models remains an ongoing research. In this paper, we propose a fully automated method to generate 2D floorplan computer-aided designs (CADs) from 3D point clouds. The proposed method consists of two main parts. The first is to detect planes in buildings, such as walls, floors, and ceilings, from unstructured 3D point clouds and to classify them based on the Manhattan-World (MW) assumption. The second is to generate 3D BIM in the industry foundation classes (IFC) format and a 2D floorplan CAD using the proposed line-detection algorithm. We experimented the proposed method on 3D point cloud data from a university building, residential houses, and apartments and evaluated the geometric quality of a wall reconstruction. We also offer the source code for the proposed method on GitHub.

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Section: Backgrounds and Related Workmentioning

confidence: 99%

Automatic 2D Floorplan CAD Generation from 3D Point Clouds

Gankhuyag

Han

2020

Applied Sciences

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“…A set of primitives is detected that replaces the point representation with the purpose of data reduction. Typically, lines are used in 2D methods and planes or cylinders are used in 3D methods (Vo et al, 2015, Lin et al, 2015, Fan et al, 2017, Vosselman and Rottensteiner, 2017. Next, the segments are classified by reasoning frameworks exploiting local and contextual information ( Fig.…”

Section: Background and Related Workmentioning

confidence: 99%

Clustering of Wall Geometry From Unstructured Point Clouds

Bassier

Vergauwen

2019

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

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Abstract. The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is retrieving the proper observations for each object. After segmenting and classifying the initial point cloud, the labeled segments should be clustered according to their respective objects. However, this procedure is challenging due to noise, occlusions and the associativity between different objects. This is especially important for wall geometry as it forms the basis for further BIM reconstruction. In this work, a method is presented to automatically group wall segments derived from point clouds according to the proper walls of a building. More specifically, a Conditional Random Field is employed that evaluates the context of each observation in order to determine which wall it belongs too. The emphasis is on the clustering of highly associative walls as this topic currently is a gap in the body of knowledge. First a set of classified planar primitives is obtained using algorithms developed in prior work. Next, both local and contextual features are extracted based on the nearest neighbors and a number of seeds that are heuristically determined. The final wall clusters are then computed by decoding the graph and thus the most likely configuration of the observations. The experiments prove that the used method is a promising framework for wall clustering from unstructured point cloud data. Compared to a conventional region growing method, the proposed method significantly reduces the rate of false positives, resulting in better wall clusters. A key advantage of the proposed method is its capability of dealing with complex wall geometry in entire buildings opposed to the presented methods in current literature.

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Section: Background and Related Workmentioning

confidence: 99%

Ifcwall Reconstruction From Unstructured Point Clouds

Bassier

Klein

Genechten

et al. 2018

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

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The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data. In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

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A self-adaptive segmentation method for a point cloud

Cited by 27 publications

References 21 publications

Automatic 2D Floorplan CAD Generation from 3D Point Clouds

Automatic 2D Floorplan CAD Generation from 3D Point Clouds

Clustering of Wall Geometry From Unstructured Point Clouds

Ifcwall Reconstruction From Unstructured Point Clouds

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