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

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ALTERNATIVE METHODOLOGIES FOR THE ESTIMATION OF LOCAL POINT DENSITY INDEX: MOVING TOWARDS ADAPTIVE LIDAR DATA PROCESSING