Digital Documentation of Heritage Objects on Non-Developable Surfaces

Pastucha, Elżbieta; Rzonca, A.; Szombara, Stanisław

doi:10.1109/bgc-geomatics.2018.00036

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…-environmental mapping and assessment of the state of the natural environment Chiappini et al (2022), Wężyk et al (2019), Balestra et al (2022), Przewoźna et al (2021), Kovanič e. al. (2020b), Apollo et al (2023), Krok et al (2020); -documentation of cultural heritage Herrero-Tejedor et al (2023), Warchoł and Lęcznar (2022), Sobura et al (2023), Gawronek et al (2017), Liu, et al (2023), Rzonca (2018), Prokop et al (2021), Gawronek and Noszczyk (2023), Bieda et al (2021), Skrzypczak et al (2023); -inventories of building objects to create 3D BIM (Building Information Modeling) models Skrzypczak et al (2022), Colucci et al (2021);…”

Section: Introductionmentioning

confidence: 99%

Selected Qualitative Aspects of Lidar Point Clouds: Geoslam Zeb-Revo and Faro Focus 3d X130

Warchoł,

Karaś,

Antoń

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents a comparison of LiDAR point clouds acquired using two, different measurement techniques: static TLS (Terrestrial Laser Scanning) performed with a FARO Focus3D X130 laser scanner and a SLAM-based (Simultaneous Localization and Mapping) unit of MLS (Mobile Laser Scanning), namely GeoSLAM ZEB-REVO. After the two point clouds were brought into a single coordinate system, they were compared with each other in terms of internal accuracy and density. The density aspect was visualized using 2D density rasters, and calculated using 3 methods available in CloudCompare software. Thus, one should consider before choosing how to acquire a LiDAR point cloud whether a short measurement time is more important (ZEB-REVO) or whether higher density and measurement accuracy is more important (FARO Focus3D X130). In BIM/HBIM modeling applications, logic dictates that the TLS solution should be chosen, despite the longer data acquisition and processing time, but with a cloud with far better quality parameters that allow objects on the point cloud to be recognized. In a situation where the TLS point cloud is 20 times more dense, it allows to model objects at the appropriate level of geometric detail.

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