Automatic fusion of photogrammetric imagery and laser scanner point clouds

Fusion of close range photogrammery and the relatively new technology of terrestrial laser scanning methods offer new opportunities for photorealistic 3D models presentation, classification of real world objects and virtual reality creation (fly through). Laser scanning technology could be seen as a complement to close-range photogrammetry. For instance, terrestrial laser scanners (TLS) have the ability to rapidly collect high-resolution 3D surface information of an object. The same type of data could be generated using close range photogrammetric (CRP) techniques, but image disparities common to close range scenes makes this an operator intensive task. The imaging systems of some TLSs do not have very high radiometric resolution whereas high-resolution digital cameras used in modern CRP do. Finally, TLSs are essentially Earth-bound whereas cameras can be moved at will around the object being imaged. This paper presents the result of an initial study into the fusion of terrestrial laser scanner generated 3D data and high-resolution digital images. Three approaches for their fusion have been investigated- data fusion which integrates data from the sensors to create synthetic perspective imagery; image fusion (synthetic perspective imagery and the intensity images); and model-based image fusion (2D intensity image and the 3D geometric model). Image registration, which includes feature detection and feature correspondence matching, is performed prior to fusion, to determine the relative rotation and translation of the digital camera relative to the laser scanner. To overcome the differences in datasets, a feature and area based matching algorithm was successfully developed and implemented. Some results of measurements on interest points and correspondence matching are presented. The result of the initial study shows that most promise is offered by model-based approaches