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Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One

1
Department of Computer Science, National Textile University, Faisalabad 37600, Pakistan
2
Department of Computer Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
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School of Information, Systems and Modeling, University of Technology, Sydney, NSW 2007, Australia
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Department of Computer Science, Government College University, Faisalabad 38000, Pakistan
5
Department of Computer Science, COMSATS University, Islamabad, Lahore Campus, Lahore 5400, Punjab, Pakistan
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(2), 209; https://doi.org/10.3390/sym11020209
Received: 10 January 2019 / Revised: 25 January 2019 / Accepted: 6 February 2019 / Published: 12 February 2019
The availability of cheap depth range sensors has increased the use of an enormous amount of 3D information in hand-held and head-mounted devices. This has directed a large research community to optimize point cloud storage requirements by preserving the original structure of data with an acceptable attenuation rate. Point cloud compression algorithms were developed to occupy less storage space by focusing on features such as color, texture, and geometric information. In this work, we propose a novel lossy point cloud compression and decompression algorithm that optimizes storage space requirements by preserving geometric information of the scene. Segmentation is performed by using a region growing segmentation algorithm. The points under the boundary of the surfaces are discarded that can be recovered through the polynomial equations of degree one in the decompression phase. We have compared the proposed technique with existing techniques using publicly available datasets for indoor architectural scenes. The results show that the proposed novel technique outperformed all the techniques for compression rate and RMSE within an acceptable time scale. View Full-Text
Keywords: 3D point cloud; compression; decompression; polynomials 3D point cloud; compression; decompression; polynomials
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Imdad, U.; Asif, M.; Ahmad, M.T.; Sohaib, O.; Hanif, M.K.; Chaudary, M.H. Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One. Symmetry 2019, 11, 209.

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