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A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain

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Document pages: 6 pages

Abstract: Automatic processing of 3D Point Cloud data for object detection, trackingand segmentation is the latest trending research in the field of AI and DataScience, which is specifically aimed at solving different challenges ofautonomous driving cars and getting real time performance. However, the amountof data that is being produced in the form of 3D point cloud (with LiDAR) isvery huge, due to which the researchers are now on the way inventing new datacompression algorithms to handle huge volumes of data thus generated. However,compression on one hand has an advantage in overcoming space requirements, buton the other hand, its processing gets expensive due to the decompression,which indents additional computing resources. Therefore, it would be novel tothink of developing algorithms that can operate analyse directly with thecompressed data without involving the stages of decompression and recompression(required as many times, the compressed data needs to be operated or analyzed).This research field is termed as Compressed Domain Processing. In this paper,we will quickly review few of the recent state-of-the-art developments in thearea of LiDAR generated 3D point cloud data compression, and highlight thefuture challenges of compressed domain processing of 3D point cloud data.

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