Traditional photogrammetric activities such as orientation, triangulation, and object
space reconstruction have been relying on distinct points in their underlying
operations. With the evolution of digital photogrammetry, there has been a
tremendous interest in utilizing linear features in various photogrammetric
activities. This interest has been motivated by the fact that the extraction of linear
features from the image space is easier to automate than distinct points. On the other
hand, object space linear features can be directly derived form terrestrial Mobile
Mapping Systems (MMS), GIS databases, and/or existing maps. Moreover,
automatic matching of linear features, either within overlapping images or between
image and object space, is easier than that of distinct points. Finally, linear features
possess more semantic information than distinct points since they most probably
correspond to object boundaries. Such semantics can be automatically identified in
imagery to facilitate higher-level tasks (e.g., surface reconstruction and object
recognition). This paper summarizes the use of linear features, which might be
represented by analytical functions (e.g., straight-line segments) or irregular (freeform)
shapes, in photogrammetric activities such as automatic space resection,
photogrammetric triangulation, camera calibration, image matching, surface
reconstruction, image-to-image registration, and absolute orientation. Current
progress, future expectations, and possible research directions are discussed as well
