IMAGE-BASED CONTROL POINT DETECTION AND CORRESPONDENCE-FREE GEOREFERENCING
Keywords: Control Point Detection, Semantic Segmentation, Deep Learning, Georeferencing, Point Cloud Registration
Abstract. In order to appropriately measure properties in 3D models, accurate georeferencing plays a vital role in structural health monitoring. For that purpose, control points are attached to the surface of the structure and measured geodetically. These points can be recovered in the virtual model and associated with the geodetic measurements. Automating the process of detecting and associating control points and geodetic measurements, facilitates the accurate georeferencing of large 3D models. While the number of marker types for control points is steadily increasing, this work claims that – under a plausible assumption – comparatively simple and commonly used marker designs can serve for accurate and robust georeferencing. By assuming that control points are asymmetrically distributed over the surface of the structure, the correspondence of points is determined by their geometric interrelation. In this work, an image-based detector for relatively simple control point types is proposed, applying transfer learning on hierarchical multi-scale attention (HMA) (Tao et al., 2020). For associating detected and geodetically measured points, a RANSAC-based procedure is presented that determines a geometrically consistent transformation between detected and measured points.