Alternative Methodologies for the Estimation of Local Point Density Index: Moving Towards Adaptive Lidar Data Processing

Abstract

ABSTRACT:Over the past few years, LiDAR systems have been established as a leading technology for the acquisition of high density point clouds over physical surfaces. These point clouds will be processed for the extraction of geo-spatial information. Local point density is one of the most important properties of the point cloud that highly affects the performance of data processing techniques and the quality of extracted information from these data. Therefore, it is necessary to define a standard methodology for the estimation of local point density indices to be considered for the precise processing of LiDAR data. Current definitions of local point density indices, which only consider the 2D neighbourhood of individual points, are not appropriate for 3D LiDAR data and cannot be applied for laser scans from different platforms. In order to resolve the drawbacks of these methods, this paper proposes several approaches for the estimation of the local point density index which take the 3D relationship among the points and the physical properties of the surfaces they belong to into account. In the simplest approach, an approximate value of the local point density for each point is defined while considering the 3D relationship among the points. In the other approaches, the local point density is estimated by considering the 3D neighbourhood of the point in question and the physical properties of the surface which encloses this point. The physical properties of the surfaces enclosing the LiDAR points are assessed through eigen-value analysis of the 3D neighbourhood of individual points and adaptive cylinder methods. This paper will discuss these approaches and highlight their impact on various LiDAR data processing activities (i.e., neighbourhood definition, region growing, segmentation, boundary detection, and classification). Experimental results from airborne and terrestrial LiDAR data verify the efficacy of considering local point density variation for precise LiDAR data processing.

Feature relevance assessment for the semantic interpretation of 3D point cloud data

“…As façades and poles are usually oriented in vertical direction, further features can be derived by projecting all 3D points onto a ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013 ISPRS Workshop Laser Scanning 2013, 11 -13 November 2013, Antalya, Turkey horizontally oriented plane P. In particular, the local point density D2D in the new 2D representation can be derived based on the radius r k-NN,2D of the circular neighborhood defined by a 2D point and its k neighbors (Lari and Habib, 2012). Additionally, the eigenvalues λ1,2D and λ2,2D of the structure tensor S2D in 2D as well as their ratio…”

“…Here, the definition of the local point density has been adapted from the respective definition in 2D (Lari and Habib, 2012).…”

Calculation of Target-Specific Point Distribution for 2D Mobile Laser Scanners

“…To include this third dimension, [12] projected all points per m 3 onto a 2D circle. In [13] the authors also discussed point distribution and ways to define it for 3D data captured from multiple platforms, further reinforcing the link between point distribution and automated data processing. Part of the difficulty in defining point distribution is due to the fact that additional research is required to calculate what point distribution different platforms are capable of, particularly for project managers designing survey specifications.…”

MIMIC: An Innovative Methodology for Determining Mobile Laser Scanning System Point Density

“…The number of points that strike a target (and therefore lead to a laser return) is predominantly influenced by the scanner hardware, the range to target and the target size. In their work on aerial and terrestrial LiDAR, the authors in [20] discuss point density, ways to define it for 3D data captured by multiple platforms and also the importance of point density during data processing. Assessing MMS performance has been approached using different methods, such as manufacturer hardware specifications [1], manual measurements using real-world MMSs [21,22] (although system and target-specific, this method has been advanced by benchmarking multiple mobile mapping systems [23]) or through LiDAR simulations.…”