Generation method of normal vector from disordered point cloud

Zheng, Dehua; Xu, Jun; Chen, Renxi

doi:10.1109/urs.2009.5137560

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…In particular, feature extraction, registration and simplification are the most important visual processing tools recently worked upon in the rapid prototyping community. In (Mérigit et al, 2011;Novatnack and Nishino, 2007;Zhao et al, 2010;Zheng et al, 2009), normal estimation and corner extraction over unorganized point sets are algorithmically defined in order to perform UPS registration (Lin and He, 2011;Myronenko and Song, 2010;Rusu et al, 2008) or simplification (Sareen et al, 2009;Song et al, 2009;Xiao and Huang, 2010) for instance. A lot of works also deal with surface segmentation issues like in (Douillard et al, 2010;Huang & Menq, 2001;Jagannathan & Miller, 2007;Rabbani et al, 2006).…”

Section: Unorganized Point Set Filteringmentioning

confidence: 99%

Point Set Analysis: An Image Analysis Point of View for Rapid Prototyping Technologies

Loménie¹,

Racoceanu²,

Stamon³

2011

Rapid Prototyping Technology - Principles and Functional Requirements

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Section: Unorganized Point Set Filteringmentioning

confidence: 99%

Point Set Analysis: An Image Analysis Point of View for Rapid Prototyping Technologies

Loménie¹,

Racoceanu²,

Stamon³

2011

Rapid Prototyping Technology - Principles and Functional Requirements

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“…The normal vectors of the neighborhood planes of laser points in the point cloud of a building roof are acquired and treated as the clustering objects. Several methods were proposed to obtain the neighborhood planes, e.g., the same radius circle method [15], K-nearest neighborhood (K-nn) method [16], triangulated irregular network (TIN) method [17] and Voronoi mesh method [18]. Generally, the Voronoi mesh method is a recommendable choice [14].…”

Section: Introductionmentioning

confidence: 99%

A new method for building roof segmentation from airborne LiDAR point cloud data

Kong¹,

Xu²,

Li³

2013

Meas. Sci. Technol.

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A new method based on the combination of two kinds of clustering algorithms for building roof segmentation from airborne LiDAR (light detection and ranging) point cloud data is proposed. The K-plane algorithm is introduced to classify the laser footprints that cannot be correctly classified by the traditional K-means algorithm. High-precision classification can be obtained by combining the two aforementioned clustering algorithms. Furthermore, to improve the performance of the new segmentation method, a new initialization method is proposed to acquire the number and coordinates of the initial cluster centers for the K-means algorithm. In the proposed initialization method, the geometrical planes of a building roof are estimated from the elevation image of the building roof by using the mathematical morphology and Hough transform techniques. By calculating the number and normal vectors of the estimated geometrical planes, the number and coordinates of the initial cluster centers for the K-means algorithm are obtained. With the aid of the proposed initialization and segmentation methods, the point cloud of the building roof can be rapidly and appropriately classified. The proposed methods are validated by using both simulated and real LiDAR data.

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Weld Defect Detection and Recognition System Based on Static Point Cloud Technology

Changzhi

Liu

Huang

et al. 2022

Artificial Intelligence

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Generation method of normal vector from disordered point cloud

Cited by 5 publications

References 2 publications

Point Set Analysis: An Image Analysis Point of View for Rapid Prototyping Technologies

Point Set Analysis: An Image Analysis Point of View for Rapid Prototyping Technologies

A new method for building roof segmentation from airborne LiDAR point cloud data

Weld Defect Detection and Recognition System Based on Static Point Cloud Technology

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