@inproceedings{Meierhold-2010-AFM,
    affiliation = {Professur für Computergraphik und Visualisierung},
    title = {Automatic Feature Matching Between Digital Images And 2D Representations Of A 3D Laser Scanner Point Cloud},
    journal = {Proceedings of the ISPRS Commission V Mid-Term Symposium 'Close Range Image Measurement Techniques'},
    keywords = {Feature matching, Terrestrial laser scanning, Intensity image, Data fusion, SIFT, Fundamental matrix, RANSAC },
    year = {2010},
    peerreview = {Nein},
    openaccess = {Nein},
    url = {http://tu-dresden.de/die_tu_dresden/fakultaeten/fakultaet_informatik/smt/cgv/publikationen/2010/87.pdf},
    author = {Meierhold, Nadine and Spehr, Marcel and Schilling, Anita and Gumhold, Stefan and Maas, Hans-Gerd},
    areas = {areasa,areasu},
    abstract = {The geometric referencing of digital image data and 3D point clouds e.g. given by a terrestrial laser scanner is the prerequisite for 
different levels of integrated data interpretation such as point cloud or mesh model texture colourisation for visualisation purposes, 
interactive object modelling by monoplotting-like procedures or automatic point cloud interpretation. Therein, the characteristics of 
laser scanner data and camera data can be regarded as complementary, so that these data are suitable for a combined interpretation. 
A precondition for the geometric referencing between laser scanner data and digital images and consequently for an integrated use of 
both data sets is the extraction of corresponding features. With a set of corresponding features the orientation of the image to the 
point cloud can be obtained by spatial resection. With regard to a high automation level the paper presents an approach for finding 
correspondences between features extracted from laser scanner data and digital images automatically. 
The basic idea of the presented approach is to use the SIFT operator to detect corresponding points in the camera image and an 
intensity image of the laser scanner data. Determining correspondences consists of four steps: Detection of salient characteristics, 
description of the features, matching of the descriptions in both images and evaluation of correct matches. RANSAC is used to find 
sets of consistent matches. The approach is validated with a data set taken from a baroque building in Dresden.}
}