Abstract
Abstract. Modern data acquisition with active or passive photogrammetric imaging techniques generally results in 3D point clouds. Depending on the acquisition or processing method, the spacing of the individual points is either uniform or irregular. In the latter case, the neighbourhood definition like for digital images (4- or 8-neighbourhood, etc.) cannot be applied. Instead, analysis requires a local point neighbourhood. The local point neighbourhood with conventional k-nearest neighbour or fixed distance searches often produce sub-optimal results suffering from the inhomogeneous point distribution. In this article, we generalize the neighbourhood definition and present a generic spatial search framework which explicitly deals with arbitrary point patterns and aims at optimizing local point selection for specific processing tasks like interpolation, surface normal estimation and point feature extraction, spatial segmentation, and such like. The framework provides atomic 2D and 3D search strategies, (i) k-nearest neighbour, (ii) region query, (iii) cell based selection, and (iv) quadrant/octant based selection. It allows to freely combine the individual strategies to form complex, conditional search queries as well as specifically tailored point sub-selection. The benefits of such a comprehensive neighbourhood search approach are showcased for feature extraction and surface interpolation of irregularly distributed points.
Highlights
Point clouds are sets of 3D points, which may potentially feature additional attributes
3D point clouds are commonly accepted as a prime product of both active and passive photogrammetry
Using sampkNN16 neighbourhood of the proposed framework, correct normal vectors can be derived. In this manuscript a spatial search framework is proposed that supports a variety of different atomic search strategies in 2D and 3D
Summary
Point clouds are sets of 3D points (xyz), which may potentially feature additional attributes. As the point cloud constitutes an unordered set, no explicit spatial topology exists in general. This is different to triangulations of point clouds or grids of 3D points. While point clouds were long regarded as an intermediate product, e.g., for the derivation of digital surface and terrain models (DSM/DTM) or 3D models (e.g. in building information modeling, BIM), they represent a stand-alone product today and play an important role in visualization, data processing, and GIS. A fundamental step for processing point clouds is the extraction of information in the vicinity of a point. Such a point can be, but is not required to be, part of the given point cloud. Finding neighbours is of paramount importance in point cloud processing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
