Extracting 3d Edgel Hypotheses from Multiple Calibrated Images: a Step towards the Reconstruction of Curved and Straight Object Boundary Lines

This paper describes a novel technique to extract elementary linear elements from multiple views, often called edgels, that are 3D points with tangent direction. Techniques based on stereopair feature, edgel or contour, matching have been intensively developed in the 80s. The relatively poor quality of images used at the time and the ill-posed stereo context contributed to focus the research around segment matching, which provided fairly good results on objects with rectilinear boundaries. Here, we take advantage of the multiplication and of the high quality of images provided by a digital frame camera to revisit this feature matching technique and to enhance the accuracy, the detection and the robustness of a 3D elementary edgel feature estimator. 3D edgels introduce the lowest implicit modelisation as possible, thus allowing the characterisation and reconstruction of curved and straight linear structures which are widespread in our man-made world. The 3D points with tangent directions generated by our estimator can be directly injected in a surface reconstruction framework based on tensor voting (Tang and Medioni, 1998). In a more practical and photogrammetric context, e.g. HR aerial images of urban landscapes, these low-level features can be used to build 3D lines that can be injected as constraint lines in addition to other features, such as 3D points and 3D segments, in a triangulation process (Paparoditis et al., 2001) to improve the surface reconstruction of manmade objects (buildings, pavements, roads, etc.). These features can also be used to construct higher level features such as planar facets in aerial images of urban areas for building reconstruction.