Implicit LOD using points ordering for processing and visualisation in Point Cloud Servers
This addresses the problem of handling billion-point lidar datasets for applications and visualization, offering a portable solution within Point Cloud Servers.
The authors tackled the challenge of processing and visualizing massive point clouds by proposing a new paradigm for implicit Level of Details (LOD) using points ordering, eliminating the need for external LOD files and enabling applications like visualization, algorithm acceleration, and density peak detection.
Lidar datasets now commonly reach Billions of points and are very dense. Using these point cloud becomes challenging, as the high number of points is intractable for most applications and for visualisation.In this work we propose a new paradigm to easily get a portable geometric Level Of Details (LOD) inside a Point Cloud Server.The main idea is to not store the LOD information in an external additional file, but instead to store it implicitly by exploiting the order of the points.The point cloud is divided into groups (patches). These patches are ordered so that their order gradually provides more and more details on the patch. We demonstrate the interest of our method with several classical uses of LOD, such as visualisation of massive point cloud, algorithm acceleration, fast density peak detection and correction.