It is known to create three-dimensional models of physical surfaces, such as a forest, an urban area or the terrain of a utility corridor, from a location sampling of the physical surface obtained using a three-dimensional scanning range finder. For example, such measurements may be obtained by flying over the terrain of interest in an aircraft and scanning the ground surface using a scanning laser range finder such as “LiDAR” (Light Detection and Ranging).
The resulting data, once processed, will take the form of a group of spaced-apart, discrete, measured surface points (a “point cloud”) representing the physical surface in a three-dimensional coordinate reference frame (typically Cartesian). The spacing between the measured surface points results from the inherent limitations in resolution of the scanning range finder, and can be significant when the physical surface being scanned is large and the details of the physical surface are fine, such as when conducting an aerial survey of an urban area that is required to capture fine architectural details of individual buildings. Moreover, commercially available three-dimensional scanning range finders for terrain measurement only measure depth and not color. As a result, the visual representation generated from an airborne three-dimensional scan of a ground surface appears as a collection of uncolored or, at best, monochrome (gray level toned) dots, which can make it difficult to resolve details that are distinguished more by color than by shape. Although photographic images can be overlaid on models based on geometric solids that are derived from the surface samples, the derivation of such models is a lengthy process that requires considerable manual intervention. While there is an emerging technique known as “flash LiDAR” that obtains both range and color information, it is currently much more expensive than conventional LiDAR and does not provide sufficient surface detail or range accuracy for many applications.