1) Digital imagery is achieving increasingly high resolutions. We are now at a stage where airborne sensors can achieve higher than 5 centimeter pixel resolution.
2) Many softcopy auto-correlation systems (XYZ terrain point matching system) were initially developed upwards of a decade ago, and were not designed to take advantage of high resolution sensors. Our own LPS ATE module was originally released in 2001 as "OrthoBase Pro" (timeline here). One of the features of some more modern systems is the ability to attempt correlation on every pixel - which can yield a very large volume of data.
3) TINs and Grids, the traditional formats for persisting terrain data in softcopy photogrammetry and GIS, may not be optimal for high resolution terrain data: hundreds of millions of points at a high density. Both have pro's and cons, which Gene Roe has outlined here. Grids can be redundant (particularly for flat regions) and while TINs are very flexible in this regard, they have no standard format. Each vendor has their own implementation, making data translation and transportability a challenge - not to mention long-term storage.
4) The LAS format, while designed for use with LIDAR sensors, may be a viable alternative to TINs and Grids for autocorrelated terrain data. Why? There are a few different reasons:
- LAS is an ASPRS-administered standard and has a high adoption rate among geospatial software vendors.
- The LAS 1.2 specification supports attribution, for example the ability to encode an RGB value for each terrain point. While it isn't commonly used within the LIDAR community, it is very useful for auto-correlated terrain. This allows RGB-encoded terrain to be used for applications such as visualization. Capabilities such as this are not possible with the traditional TIN/Grid approach.
- When correlating on every image pixel, terrain data can be very dense. A compelling research area involves applying LIDAR classification and filtering techniques to autocorrelated terrain data.
The images above show color attribute encoding for an LAS 1.2 point cloud that was processed from stereo ADS80 imagery. The bottom image is a zoomed in perspective view showing a lot of detail: solar panels on the roof, cars, and a feeling of depth in the empty pool. These images show the point cloud rendered as a TIN within the FugroViewer. As you can see, the terrain representation is quite different from a traditional TIN or grid.