Robust Segmentation in Laser Scanning 3D Point Cloud Data

Nurunnabi, Abdul; Belton, David; West, Geoff

doi:10.1109/dicta.2012.6411672

Cited by 125 publications

(128 citation statements)

References 24 publications

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“…However, the integration of knowledge is still rare, with few example of hybrid pipelines [83,84]. Our proposed approach constitute a hybrid method inspired by previous work in shape recognition [85][86][87][88], region growing pipelines [80,89,90] and abstraction-based segmentation [91][92][93][94][95] relying on 3D connected component labelling and voxel-based segmentation. As such, different features presented in Table 1 constitute the base for segmentation.…”

Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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Digital investigations of the real world through point clouds and derivatives are changing how curators, cultural heritage researchers and archaeologists work and collaborate. To progressively aggregate expertise and enhance the working proficiency of all professionals, virtual reconstructions demand adapted tools to facilitate knowledge dissemination. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. In this paper, we review the state of the art of point cloud integration within archaeological applications, giving an overview of 3D technologies for heritage, digital exploitation and case studies showing the assimilation status within 3D GIS. Identified issues and new perspectives are addressed through a knowledge-based point cloud processing framework for multi-sensory data, and illustrated on mosaics and quasi-planar objects. A new acquisition, pre-processing, segmentation and ontology-based classification method on hybrid point clouds from both terrestrial laser scanning and dense image matching is proposed to enable reasoning for information extraction. Experiments in detection and semantic enrichment show promising results of 94% correct semantization. Then, we integrate the metadata in an archaeological smart point cloud data structure allowing spatio-semantic queries related to CIDOC-CRM. Finally, a WebGL prototype is presented that leads to efficient communication between actors by proposing optimal 3D data visualizations as a basis on which interaction can grow.

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Section: Knowledge-based Detection and Classificationmentioning

confidence: 99%

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Poux

Neuville

Wersch³

et al. 2017

Geosciences

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“…Hence, the first PC ( 2 ) shows the highest ( 2 %) variability of the data. Although PCA has been used successfully in point cloud processing, the results from PCA are highly influenced by outliers and produce misleading results (Nurunnabi et al, 2014(Nurunnabi et al, , 2015(Nurunnabi et al, , 2016.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…It fits a cylinder model to pre-processed partial cylindrical data. Pre-processing could for example consist of a segmentation step (Nurunnabi et al, 2016). The method is statistically robust and consistent, produces reliable results in the presence of a high percentage of clustered outliers for incomplete as well as full data.…”

Section: Introductionmentioning

confidence: 99%

Robust Cylinder Fitting in Three-Dimensional Point Cloud Data

Nurunnabi

Sadahiro

Lindenbergh

2017

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

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ABSTRACT:This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

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“…The segmentation data can be extracted from terrestrial laser scanning data in the pre-processing stage (Nurunnabi et al, 2012). As shown in Figure 2, the entire reconstruction process has been decomposed into two individual steps in order to reduce the overall complexity.…”

Section: Grammar-based Automatic Model Reconstructionmentioning

confidence: 99%

Evaluation of Model Recognition for Grammar-Based Automatic 3d Building Model Reconstruction

Qian¹,

Helmholz²,

Belton³

2016

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

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ABSTRACT:In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.

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Robust Segmentation in Laser Scanning 3D Point Cloud Data

Cited by 125 publications

References 24 publications

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

3D Point Clouds in Archaeology: Advances in Acquisition, Processing and Knowledge Integration Applied to Quasi-Planar Objects

Robust Cylinder Fitting in Three-Dimensional Point Cloud Data

Evaluation of Model Recognition for Grammar-Based Automatic 3d Building Model Reconstruction

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