Iris Publishers - Global Journal of Engineering Sciences (GJES)

What’s new in Point Cloud Compression?


Authored by Chao CAO


3D point cloud is a simple data structure representing both static and dynamic 3D objects. Though providing close-to-reality visualization, point clouds with high density raise massive demand for storage while sparse point clouds used for navigation require high precision. The emergency of compression technologies is mandatory in the form of standardization. A walkthrough for the technical approaches exploited in the two frameworks (V-PCC and G-PCC) under the MPEG standardization process is presented. Though with certain limitations, the achieved promising compression performances indicate a foreseeable evolution of the standard and a bright future for point cloud compression technologies.


With the advancement of 3D data acquisition technologies, it is becoming easier to reconstruct point clouds from real-world objects. A 3D point cloud is defined as a set of 3D points, where each point is described by its Cartesian coordinates and various attributes such as colors or normal as shown in Figure 1.

A Point Cloud is considered a relatively simple 3D representation allowing a realistic visualization and can be captured by various setups. For example, Microsoft’s Kinect and Apple’s PrimeSense are now being used in many interactive mobile applications to capture 3D scenes and models. Light Detection And Ranging (LiDAR) is another well-known technology for acquiring point clouds. The realism and precision of the captured objects are obtained with the cost of huge amount of points to be store/transmitted. In order to make Point Cloud useful in applications, compression is required.

The development of compression technologies for point cloud has gained increasing attention lately in the research community to make them suitable for real-time, portable applications such as autonomous navigation [1] and Virtual and Augmented Reality [2]. A “Universe Map” for point cloud compression techniques is proposed in [3] and illustrated in Figure 2.

 

To read more about this article: https://irispublishers.com/gjes/fulltext/whats-new-in-point-cloud.ID.000598.php

 

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