2016/02/22 by Rémi Cura, Cura, Rémi, Julien Perret +3 · 1 citation
Computer Science · Earth and Planetary Sciences · Environmental Science · #3D Surveying and Cultural Heritage #Computational Geometry (cs.CG) #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Software Engineering (cs.SE) #cs.CG #cs.CV #cs.SE
paper · pdf · doi:10.48550/arxiv.1602.06920
this article is a split of the previous one because the previous article covered two topics to lousily related
openalex publication_date 2016/02/22 · arxiv created 2018/01/11 · arxiv updated 2018/01/15 · openalex created_date 2018/01/26 · openalex updated_date 2026/07/28
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.