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Efficient Point Cloud Pre-processing using The Point Cloud Library
Marius Miknis, Ross Davies, Peter Plassmann, Andrew Ware
Pages - 63 - 72     |    Revised - 30-04-2016     |    Published - 01-06-2016
Volume - 10   Issue - 2    |    Publication Date - June 2016  Table of Contents
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KEYWORDS
Point Cloud, Point Cloud Library, Point Data Pre-processing.
ABSTRACT
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
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Mr. Marius Miknis
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom
Marius.miknis@southwales.ac.uk
Dr. Ross Davies
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom
Dr. Peter Plassmann
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom
Professor Andrew Ware
Faculty of Computing, Engineering and Science, University of South Wales - United Kingdom