Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach

Potamias, Rolandos Alexandros; Bouritsas, Giorgos; Zafeiriou, Stefanos

doi:10.1007/978-3-031-20086-1_34

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Reducing Point Density Variations In Point Clouds1

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2023

2024

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Cited by 9 publications

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“…Decimation techniques range from simple ones, such as random sampling, to complex decimations based on shape of the objects described by the point cloud [ 82 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ]. Random sampling can be based on the ordinal number of a point within the point cloud (count-based decimation).…”

Section: Reducing Point Density Variations In Point Cloudsmentioning

confidence: 99%

Point Density Variations in Airborne Lidar Point Clouds

Petras

Petrášová

McCarter

et al. 2023

Sensors

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In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit point density variations. Some of these density variations indicate issues with point clouds, potentially leading to errors in derived products. To highlight these issues, we provide an overview of point density variations and show examples in six airborne lidar point cloud datasets that we used in our topographic and geospatial modeling research. Using the published literature, we identified sources of point density variations and issues indicated or caused by these variations. Lastly, we discuss the reduction in point density variations using decimations, homogenizations, and their applicability.

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