Point-based computing on scanned terrain with LidarViewer

Oliver Kreylos, Michael E. Oskin, Eric S. Cowgill, Peter O. Gold, & Austin J. Elliott

Published 2011, SCEC Contribution #1478

As an alternative to grid-based approaches, point-based computing offers access to the full information stored in unstructured point-clouds derived from lidar scans of terrain. By employing appropriate hierarchical data structures and algorithms for out-of-core processing and view-dependent rendering, it is feasible to visualize and analyze 3D lidar point cloud data sets of arbitrary sizes in real time. Here we describe LidarViewer, an implementation of point-based computing developed at the UC Davis W.M. Keck Center for Active Visualization in the Earth Sciences (KeckCAVES). Specifically, we show how point-based techniques can be used to simulate hillshading of a continuous terrain surface by computing local, point-centered tangent plane directions in a pre-processing step. Lidar scans can be analyzed interactively by extracting features using a selection brush. We present examples including measurement of bedding and fault surfaces, and manual extraction of 3D features such as vegetation. Point-based computing approaches can offer significant advantages over grids, including analysis of arbitrarily large data sets, scale- and direction-independent analysis and feature extraction, point-based feature- and time-series comparison, and opportunities to develop semi-automated point filtering algorithms. Because LidarViewer is open-source, and its key computational framework is exposed via a Python interface, it provides ample opportunities to develop novel point-based computation methods for lidar data.

Kreylos, O., Oskin, M. E., Cowgill, E. S., Gold, P. O., & Elliott, A. J. (2011). Point-based computing on scanned terrain with LidarViewer. Geosphere, 9(3). doi: 10.1130/GES00705.1.