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A demonstration of B-EagleV Visualizing massive point cloud directly from HDFS


Scalable and Secure Big Data I

3D Reconstruction in Canonical

Class Agnostic Image Common Ob
Abstract


The advent of Hadoop has inspired many researchers to conduct studies on big data. These studies have covered a wide range of aspects of big spatial data. However, they still face challenges in visualizing big spatial data on a distributed storage model since loading multi-resolution data is inefficient. For this reason, multi-resolution data are usually excluded from the distributed storage model to speed up the loading process. This limitation prompted the introduction of B-EagleV, a novel Hadoop-based solution that enables users to manage and visualize massive point cloud data on Hadoop Distributed File System (HDFS) without moving the multi-resolution data to a local server. This paper presents the achievements of B-EagleV in efforts to discover the values of Hadoop in visualizing massive point cloud data.

KeyWords
B-EagleV, Hadoop, Massive Point Cloud, Webbased Visualization, Point-based Rendering



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