摘要：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.
