论文标题
快速回顾3D点云数据压缩技术的最新趋势以及3D压缩域中直接处理的挑战
A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain
论文作者
论文摘要
自动处理3D点云数据以进行对象检测，跟踪和细分是AI和数据科学领域的最新趋势研究，该研究专门旨在解决自动驾驶汽车的不同挑战并获得实时性能。但是，以3D点云（带有LiDAR）形式产生的数据量非常大，因此，研究人员现在正在开发新的数据压缩算法以处理如此生成的大量数据。但是，一方面，压缩在克服空间需求方面具有优势，但另一方面，由于减压的压缩，其处理变得昂贵，这使得增加了其他计算资源。因此，考虑开发可以直接使用压缩数据操作/分析的算法而无需涉及减压和重新压缩阶段（需要多次，需要操作或分析的压缩数据）将是新颖的。该研究领域被称为压缩域处理。在本文中，我们将迅速回顾Lidar领域最新的最新开发项目产生了3D点云数据压缩，并强调了3D点云数据的压缩域处理的未来挑战。
Automatic processing of 3D Point Cloud data for object detection, tracking and segmentation is the latest trending research in the field of AI and Data Science, which is specifically aimed at solving different challenges of autonomous driving cars and getting real time performance. However, the amount of data that is being produced in the form of 3D point cloud (with LiDAR) is very huge, due to which the researchers are now on the way inventing new data compression algorithms to handle huge volumes of data thus generated. However, compression on one hand has an advantage in overcoming space requirements, but on the other hand, its processing gets expensive due to the decompression, which indents additional computing resources. Therefore, it would be novel to think of developing algorithms that can operate/analyse directly with the compressed 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 the area of LiDAR generated 3D point cloud data compression, and highlight the future challenges of compressed domain processing of 3D point cloud data.
