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Abstract
This article addresses the problem of reconstructing 3D surfaces from unorganized point sets (also known as point clouds). Thisissue is common to many different areas of science and engineering, including computer graphics and computer vision. Unorganizedpoint sets can be acquired in different ways, e.g., using laser scanners, computer tomography, magnetic resonance imaging, multicameravision systems, etc. This paper presents a method for point cloud merging that allows calculating the initial rotation andtranslation between point clouds of different viewing angles. By tracking the marker's position, scanning and point cloud registrationcan be done in real time and with high accuracy. Ill. 7, bibl. 11 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.113.7.616
Highlights
Introduction show that this technique works great as on largely overlapping point clouds as on partly overlapping point
The views are obtained 3D laser scanning system from multiple 3D scanners or from a single scanner positioned at different locations and orientation
A problem of surface registration arises, i.e. the different views have to be located in a same coordinate system
Summary
Introduction show that this technique works great as on largely overlapping point clouds as on partly overlapping point. It iteratively revises the transformation, usually composed of scale, rotation and translation, needed to minimize the distance between the points of the two clouds. Point cloud of the 3D object is computed by calculating intersection of the laser plane with a boundary of the object. In this paper we present an automatic method of point clouds merging for complete surface reconstruction.
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