Octree-based region growing for point cloud segmentation

Vo, Anh Vu; Truong-Hong, Linh; Laefer, Debra F.; Bertolotto, Michela

doi:10.1016/j.isprsjprs.2015.01.011

Cited by 542 publications

(329 citation statements)

References 29 publications

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“…Ackermann and Troisi (2010) used a region growing approach to segment planar pitched roofs in 3D point clouds for automatic 3D modelling of buildings. Anh-Vu Vo et al (2015) presented an octree-based region growing approach for a fast surface patch segmentation of urban environment 3D point clouds.…”

Section: Region Growing Segmentationmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

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

314

138

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ABSTRACT:Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.

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Section: Region Growing Segmentationmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

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

314

138

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“…Summarily, the relevant point cloud segmentation approaches can be grouped into three major categories: the model-based methods, the region growingbased methods, and the clustering-based methods (Vo et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Hoegner

Tuttas

et al. 2017

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

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ABSTRACT:Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

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“…4 was selected to illustrate the proposed method (Fig. 3a. After the data points of the structural members were separate from the larger data set (step 1.1), a region growing-based octree [19] was used to extract point clouds of the flange/web data of the structural member (step 1.2). The segmentation results are shown in Fig.…”

Section: Deformationmentioning

confidence: 99%

“…5b). A region-growing based octree method [19] was employed to extract the surface containing surface loss or holes (step 1.2) (Fig. 5c).…”

Section: Surface Lossmentioning

confidence: 99%

Documentation of Bridges by Terrestrial Laser Scanner

Truong-Hong

Laefer

2015

IABSE Reports

Self Cite

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Abstract:Bridge structures are subjected to deterioration due to excessive usage, overloading, and aging material. For the last two decades, a significant amount research has been developed for collecting data for structural health monitoring. Yet, visual investigation with an on-site inspector remains the predominant method. This is true despite the highly subjective and time consuming aspects of this approach. Alternatively, terrestrial laser scanning can acquire surface details of structures quickly and accurately and is, thus, an emerging means to overcome the shortcomings of direct visual inspection. This paper presents a procedure for data collection for bridge inspection documentation and proposes a "cell-based method" for determination of structure deterioration (involving vertical deformation and lateral distortion), as well as surface loss due to corrosion. The Guinness Bridge built in 1880s located in Dublin council, Ireland is selected as a case study to illustrate the efficacy of the proposed method.

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Octree-based region growing for point cloud segmentation

Cited by 542 publications

References 29 publications

A Review Of Point Clouds Segmentation and Classification Algorithms

A Review Of Point Clouds Segmentation and Classification Algorithms

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Documentation of Bridges by Terrestrial Laser Scanner

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