A UNIFIED FRAMEWORK FOR THE AUTOMATIC MATCHING OF POINTS AND LINES IN MULTIPLE ORIENTED IMAGES
Christian Beder · 2004
The accurate reconstruction of the three-dimensional structure from multiple images is still a challenging problem, so that most current approaches are based on semi-automatic procedures. Therefore the introduction of accurate and reliable automation for this classical problem is one of the key goals of photogrammetric research. This work deals with the problem of matching points and lines across multiple views, in order to gain a highly accurate reconstruction of the depicted object in three-dimensional space. In order to achieve this goal, a novel framework is introduced, that draws a sharp boundary between feature extraction, feature matching based on geometric constraints and feature matching based on radiometric constraints. The isolation of this three parts allows direct control and therefore better understanding of the different kinds of influences on the results. Most image feature matching approaches heavily depend on the radiometric properties of the features and only incorporate geometry information to improve performance and stability. The extracted radiometric descriptors of the features often assume a local planar or smooth object, which is by definition neither present at object corners nor edges. Therefore it would be desirable to use only descriptors that are rigorously founded for the given object model. Unfortunately the task of feature matching based on radiometric properties becomes extremely difficult for this much weaker descriptors. Hence a key feature of the presented framework is the consistent and rigorous use of statistical properties of the extracted