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MASSIVE POINT CLOUD PROCESSING ON HADOOP: CHALLENGES AND PROPOSED SOLUTION

机译:HADOOP上的大规模点云处理:挑战和建议的解决方案

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Because of the remarkable technological development of sensors and algorithms for point cloud data acquisition and registration, collecting point cloud data over large spaces has become more accessible than ever before. The result of this process is the generation of massive point cloud data, which can exceed the capacity of a single computer. Efficient visualization of this data is an important issue that needs to be addressed. Using big data platforms, such as Hadoop, could bring benefits in processing big point cloud data. However, due to some barriers, there have not been many studies conducted on this platform to solve problems of big point cloud data, so far. In this study, the potential and challenges of processing big point cloud data using Hadoop will be presented. Thereafter, a comprehensive solution will be proposed to overcome the limitations, which can result in the first Hadoop-based framework for fully processing massive point cloud data.
机译:由于用于点云数据获取和注册的传感器和算法的惊人技术发展,在大型空间上收集点云数据比以往任何时候都更加容易。此过程的结果是生成大量点云数据,该数据可能超过单台计算机的容量。此数据的有效可视化是需要解决的重要问题。使用Hadoop等大数据平台可能会带来处理大点云数据的好处。但是,由于一些障碍,到目前为止,在该平台上尚未进行很多研究来解决大点云数据的问题。在这项研究中,将介绍使用Hadoop处理大点云数据的潜力和挑战。此后,将提出一个综合解决方案来克服这些限制,这可能会导致第一个基于Hadoop的框架来完全处理海量点云数据。

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