Supporting multi-resolution out-of-core rendering of massive LiDAR point clouds through non-redundant data structures. Issue 3 (4th March 2019)

• Record Type: Journal Article
• Authors: Deibe, David; Amor, Margarita; Doallo, Ramón
• Is Part Of: International journal of geographical information science. Volume 33:Issue 3(2019)
• Page Start: 593
• Page End: 617
• Publication Date: 2019-03-04
• Subjects: LiDAR -- web-visualization -- efficient data structures -- multi-resolution -- out-of-core; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2018.1549734
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: In recent years, the evolution and improvement of LiDAR ( Light Detection and Ranging ) hardware has increased the quality and quantity of the gathered data, making the storage, processing and management thereof particularly challenging. In this work we present a novel, multi-resolution, out-of-core technique, used for web-based visualization and implemented through a non-redundant, data point organization method, which we call Hierarchically Layered Tiles (HLT), and a tree-like structure called Tile Grid Partitioning Tree (TGPT). The design of these elements is mainly focused on attaining very low levels of memory consumption, disk storage usage and network traffic on both, client and server-side, while delivering high-performance interactive visualization of massive LiDAR point clouds (up to 28 billion points) on multiplatform environments (mobile devices or desktop computers). HLT and TGPT were incorporated and tested in ViLMA (Vi sualization forL iDAR data using aM ulti-resolutionA pproach), our own web-based visualization software specially designed to work with massive LiDAR point clouds.