3D point cloud segmentation: A survey

Nguyen, Anh; Le, Bac

doi:10.1109/ram.2013.6758588

Cited by 341 publications

(228 citation statements)

References 26 publications

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“…A first attempt to group segmentation methods follows the works of Sapkota (2008) and Nguyen (2013) and a schematic representation is shown in Figure 2.…”

Section: Segmentationmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

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

316

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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“…A first attempt to group segmentation methods follows the works of Sapkota (2008) and Nguyen (2013) and a schematic representation is shown in Figure 2.…”

Section: Segmentationmentioning

confidence: 99%

A Review Of Point Clouds Segmentation and Classification Algorithms

Grilli

Menna

Remondino

2017

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

316

138

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“…RANSAC was introduced by Fischer and Bolles [30] in 1981 and is widely used for shape detection [13,20,31]. The RANSAC algorithm mainly involves performing two iteratively repeated steps on a given point cloud: generating a hypothesis and verification.…”

Section: Ransacmentioning

confidence: 99%

An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

et al. 2017

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Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.

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“…Segmentation can be also used for automatically analysing urban scenes both on aerial 3D data (Liu et al, 2015) or architectural structures starting from terrestrial data (Boulaassal et al, 2007); for separating vegetation from DTM in aerial scenes (Reitberger et al, 2009) or identifying roads (Maboudi, et al, 2016). Such process can be applied by using either 3D clouds (Nguyen, Le, 2013;Oehler et al, 2011) or meshes and volumes (Chen, Georganas, 2006;Attene et al, 2006;Ho, Chuang, 2012). For point cloud segmentation, an efficient tool is the Point Cloud Library (PLC -http://pointclouds.org/about/) that provides open sources algorithms and scripts to process 3D point cloud.…”

Section: State Of the Artmentioning

confidence: 99%

Segmentation of 3d Models for Cultural Heritage Structural Analysis – Some Critical Issues

Barsanti

Guidi

Luca

2017

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

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ABSTRACT:Cultural Heritage documentation and preservation has become a fundamental concern in this historical period. 3D modelling offers a perfect aid to record ancient buildings and artefacts and can be used as a valid starting point for restoration, conservation and structural analysis, which can be performed by using Finite Element Methods (FEA). The models derived from reality-based techniques, made up of the exterior surfaces of the objects captured at high resolution, are -for this reason -made of millions of polygons. Such meshes are not directly usable in structural analysis packages and need to be properly pre-processed in order to be transformed in volumetric meshes suitable for FEA. In addition, dealing with ancient objects, a proper segmentation of 3D volumetric models is needed to analyse the behaviour of the structure with the most suitable level of detail for the different sections of the structure under analysis. Segmentation of 3D models is still an open issue, especially when dealing with ancient, complicated and geometrically complex objects that imply the presence of anomalies and gaps, due to environmental agents such as earthquakes, pollution, wind and rain, or human factors. The aims of this paper is to critically analyse some of the different methodologies and algorithms available to segment a 3D point cloud or a mesh, identifying difficulties and problems by showing examples on different structures.

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3D point cloud segmentation: A survey

Cited by 341 publications

References 26 publications

A Review Of Point Clouds Segmentation and Classification Algorithms

A Review Of Point Clouds Segmentation and Classification Algorithms

An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells

Segmentation of 3d Models for Cultural Heritage Structural Analysis – Some Critical Issues

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