Visibility analysis for the occlusion detection and characterisation in street point clouds acquired with Mobile Laser Scanning. Issue 25 (13th December 2022)

• Record Type: Journal Article
• Authors: Barros-Ribademar, Jose; Balado, Jesús; Arias, Pedro; González-Collazo, Silvia María
• Is Part Of: Geocarto international. Volume 37:Issue 25(2023)
• Page Start: 10152
• Page End: 10169
• Publication Date: 2022-12-13
• Subjects: LiDAR -- occlusion recognition -- occlusion regeneration -- 3D data processing -- point cloud completion -- urban environment; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2032392
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Although multi-temporal acquisitions are a widely accepted way to reduce occlusions in point clouds, occlusion analysis continues to be a visual analysis. The objective of this work is the design of an automatic method for the detection and characterization of street point cloud occlusions acquired with Mobile Laser Scanning (MLS). The proposed method consists of four main phases: alignment of the point cloud, rasterization, generation of occluded point clouds and visibility analysis. The proposed method was tested with point clouds of TerraMobil-ita/iQmulus dataset. The result shows a correct detection of 98.71% of the occlusions on ground and 83.37% on façades. All detected occlusions were correctly characterized. In the case studies, cars generated the largest amount of occluded area on the ground, with an occluded area per object of 15.36 m2. The main source of occlusions on façades was the building geometry and windows.