Abstract
Abstract. In addition to the traditional Geographic Information System (GIS) data such as images and vectors, point cloud data has become more available. It is appreciated for its precision and true three-Dimensional (3D) nature. However, managing the point cloud can be difficult due to scaling problems and specificities of this data type. Several methods exist but are usually fairly specialised and solve only one aspect of the management problem. In this work, we propose a complete and efficient point cloud management system based on a database server that works on groups of points rather than individual points. This system is specifically designed to solve all the needs of point cloud users: fast loading, compressed storage, powerful filtering, easy data access and exporting, and integrated processing. Moreover, the system fully integrates metadata (like sensor position) and can conjointly use point clouds with images, vectors, and other point clouds. The system also offers in-base processing for easy prototyping and parallel processing and can scale well. Lastly, the system is built on open source technologies; therefore it can be easily extended and customised. We test the system will several billion points of point clouds from Lidar (aerial and terrestrial ) and stereo-vision. We demonstrate ~ 400 million pts/h loading speed, user-transparent greater than 2 to 4:1 compression ratio, filtering in the approximately 50 ms range, and output of about a million pts/s, along with classical processing, such as object detection.
Highlights
The last decades have seen the rise of Geographic Information System (GIS) data availability, in particular through the open data movement
We present a point cloud management system fully based on pgPointCloud (2014) and open source tools
The proposed solution relies on a PostgreSQL (2014) RDBMS server using the PostGIS (2014) and pgPointCloud (2014) extensions
Summary
The last decades have seen the rise of GIS data availability, in particular through the open data movement. Precise, and available, and the point cloud complements images naturally Due to their massive unstructured nature and limited integration with other GIS data, the management of point clouds still remains challenging. Such a database can reach billions of rows Storing this many rows is problematic because DBMSs have a non-negligible overhead per row, which reduces the scaling possibilities, regarding the time it takes to create it, to index it, or in the final space it takes. - server sends - client reads - point streaming - point cloud files as a service These limitations have been studied and inspired NoSQL databases. NoSQL database are stripped DBMSs that have been specially tailored for massive and weakly relational data They scale extremely well to many computers.
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