Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds

Kwon, Soon-Wook; Bosché, Frédéric; Kim, Chang-Wan; Haas, Carl T.; Liapi, Katherine A.

doi:10.1016/j.autcon.2003.08.007

Cited by 99 publications

(55 citation statements)

References 16 publications

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“…Kim et al, 2011;Kwon, Bosche, Kim, Haas, & Liapi, 2004;Teizer, Haas, Caldas, & Bosche, 2006;Teizer et al, 2007) and on the automated tracking of objects ( Razavi et al, 2009;Razavi & Haas, 2010). In this research, these models are referred to as object-based fusion models.…”

Section: Existing Automated Progress Tracking Modelsmentioning

confidence: 99%

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

Shahi

Cardona²,

Haas³

et al. 2012

Construction Research Congress 2012

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Section: Existing Automated Progress Tracking Modelsmentioning

confidence: 99%

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

Shahi

Cardona²,

Haas³

et al. 2012

Construction Research Congress 2012

Self Cite

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“…Their approach included generating and analyzing terrain models based on the data collected from LADARs to assess the progress of an excavation activity. Kwon et al developed an approach that fits range data to geometric primitives so that 3D as-built modeling can be achieved rapidly [19]. This approach utilizes sparse range point clouds and targets increasing the efficiency of processing the data collected to enable real-time field applications.…”

Section: Background Researchmentioning

confidence: 99%

A formalism for utilization of sensor systems and integrated project models for active construction quality control

Akinci

Boukamp

Gordon

et al. 2006

Automation in Construction

278

148

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Defects experienced during construction are costly and preventable. However, inspection programs employed today cannot adequately detect and manage defects that occur on construction sites, as they are based on measurements at specific locations and times, and are not integrated into complete electronic models. Emerging sensing technologies and project modeling capabilities motivate the development of a formalism that can be used for active quality control on construction sites. In this paper, we outline a process of acquiring and updating detailed design information, identifying inspection goals, inspection planning, as-built data acquisition and analysis, and defect detection and management. We discuss the validation of this formalism based on four case studies.

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“…Rahayem et al (2012) fit cylinders followed by ellipse fitting for data without outliers. Kwon et al (2004) developed a PCA based cylinder fitting algorithm without using the normals of the data. Lalonde et al (2006) worked in a similar way (Kwon et al, 2004), and developed two algorithms based on 2D projection and 3D fitting for tree trunk segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Kwon et al (2004) developed a PCA based cylinder fitting algorithm without using the normals of the data. Lalonde et al (2006) worked in a similar way (Kwon et al, 2004), and developed two algorithms based on 2D projection and 3D fitting for tree trunk segmentation. They showed that results from the PCA based 2D projection technique approximately 25% better than the results from 3D fitting.…”

Section: Introductionmentioning

confidence: 99%

“…Cylindrical features are one of the most encountered geometric primitives in point clouds when collected in industrial sites, urban areas, street corridors, but also forest. Cylinder fitting is a fundamental task in computer graphics, computer vision, image analysis, reverse engineering, pattern recognition, photogrammetry and remote sensing for various applications like feature extraction (De Guevara et al, 2011), surface reconstruction (Schnabel et al, 2007), archaeological documentation, street furniture management, building information modelling (Kwon et al, 2004), as-built modelling (Ahmed et al, 2014), machine tools quality control, industrial plant settings (Su et al, 2016), tunnel monitoring (Gosliga et al, 2006), and forest inventory (Lalonde et al, 2006), and therefore has been studied rigorously.…”

Section: Introductionmentioning

confidence: 99%

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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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Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds

Cited by 99 publications

References 16 publications

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

Activity-Based Data Fusion for Automated Progress Tracking of Construction Projects

A formalism for utilization of sensor systems and integrated project models for active construction quality control

Robust Cylinder Fitting in Three-Dimensional Point Cloud Data

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