Abstract
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.
Highlights
There a lot of things that can be carried out when we talk about digital imagery
We propose a novel lossy 3D point cloud compression method in which a point cloud is compressed in such a way that a set of points is represented by Polynomial equation of degree one that utilizes geometric information of scenes as highlighted by many researchers [19,20,21,22,23]
Our proposed technique is tested on 3D point cloud datasets which are described in [8] and used in state-of-the-art techniques [7,12] to measure their algorithms’ performance
Summary
There a lot of things that can be carried out when we talk about digital imagery. In this multimedia world, it is important to ensure that you offer the best visualization of scenes. The most realistic type of data is three-dimensional (3D) data which is a Point Cloud, which contains another dimension of depth along-with length and width. We are living in, moving through and seeing the world in three dimensions and a point cloud simulates objects and their environment as we see them through our own eyes. The chief concern is that a point cloud is an accurate digital record of objects and scenes. Point clouds are produced by 3D systems, like Microsoft Kinect, Light Detection and Symmetry 2019, 11, 209; doi:10.3390/sym11020209 www.mdpi.com/journal/symmetry
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