Paper
18 October 2002 Automatic segmentation and model identification in unordered 3D-point cloud
Author Affiliations +
Proceedings Volume 4902, Optomechatronic Systems III; (2002) https://doi.org/10.1117/12.467726
Event: Optomechatronic Systems III, 2002, Stuttgart, Germany
Abstract
Segmentation and object recognition in point cloud are of topical interest for computer and machine vision. In this paper, we present a very robust and computationally efficient interactive procedure between segmentation, outlier detection, and model fitting in 3D-point cloud. For an accurate and reliable estimation of the model parameters, we apply the orthogonal distance fitting algorithms for implicit curves and surfaces, which minimize the square sum of the geometric (Euclidean) error distances. The model parameters are grouped and simultaneously estimated in terms of form, position, and rotation parameters, hence, providing a very advantageous algorithmic feature for applications, e.g., robot vision, motion analysis, and coordinate metrology. To achieve a high automation degree of the overall procedures of the segmentation and object recognition in point cloud, we utilize the properties of implicit features. We give an application example of the proposed procedure to a point cloud containing multiple objects taken by a laser radar.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sung Joon Ahn, I. Effenberger, W. Rauh, Hyungsuck Cho, and E. Westkämper "Automatic segmentation and model identification in unordered 3D-point cloud", Proc. SPIE 4902, Optomechatronic Systems III, (18 October 2002); https://doi.org/10.1117/12.467726
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Cited by 3 scholarly publications.
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KEYWORDS
Clouds

3D modeling

Object recognition

Optical spheres

Visual process modeling

LIDAR

Machine vision

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