# An Automatic Geometric Registration Method for Multi Temporal 3D Models
###### Abstract  
The application research of ground change detection based on multi-temporal 3D models is attracting more and more attention. However, the conventional methods of using UAV GPS-supported bundle adjustment or measuring ground control points before each data collection are not only economically costly, but also have insufficient geometric accuracy. In this paper, an automatic geometric-registration method for multi-temporal 3D models is proposed. First, feature points are extracted from the highest resolution texture image of the 3D model, and their corresponding spatial location information is obtained based on the triangular mesh of the 3D model, which is then converted into 3D spatial-feature points. Second, the transformation model parameters of the 3D model to be registered relative to the base 3D model are estimated by the spatial-feature points with the outliers removed, and all the vertex positions of the model to be registered are updated to the coordinate system of the base 3D model. The experimental results show that the position measurement error of the ground object is less than 0.01 m for the multi-temporal 3D models obtained by the method of this paper. Since the method does not require the measurement of a large number of ground control points for each data acquisition, its application to long-period, high-precision ground monitoring projects has great economic and geometric accuracy advantages. unmanned aerial vehicles (UAVs); photogrammetry; 3D model; geometric registration; accuracy; feature points +
Footnote †: journal: Applied Sciences  
1 Thet Electrical Power Corporation Limited of CHN ENERGY Group, Linzhi 860019, China 1  
Normally, UAV imagery is geo-referenced via ground control points (GCPs), which are placed on the study area and measured using global navigation satellite system (GNSS) receivers or total stations [18]. In order to ensure that the 3D model generated from UAV images of different time sequences have a unified spatial reference, the conventional processing method is to survey a certain number of GCPs before each data acquisition. For small- or medium-sized UAV photogrammetry projects, five GCPs can meet the geometric requirements [19]. However, more GCPs are needed for large engineering projects, especially those that collect image data by terrain-following flights [20]. Arranging and measuring a large number of GCPs for construction sites, or dangerous areas that are difficult for people to reach, is costly and lacks the flexibility. As the manufacturing cost of airborne GNSS-RTK (real time kinematic) receivers decreases, it has become possible to integrate them into low-cost UAVs [21]. The camera position with centimeter accuracy can be obtained by GNSS-RTK equipment, which can be introduced into the global-position-system-supported (GPS-supported) bundle-adjustment (BA) optimization to significantly reduce the number of GCPs required, or even eliminate the GCPs completely [22; 23]. Other research results show that the accuracy of the UAV GPS-supported BA method cannot obtain centimeter-level accuracy; in particular, there may be systematic errors in the vertical direction [24; 25]. It can be seen from this research that high-precision geometric positioning for ground objects based on UAV photogrammetry technology cannot yet completely abandon the use of GCPs. In fact, the absolute position of an object is not the user's concern in many cases, but rather relative changes. It is more valuable to obtain the spatial position of the ground object on the 3D model obtained from different temporal data. Obviously, it is not economical to measure a large number of GCPs before each flight mission, and it is impossible to obtain a high-precision unified space benchmark for the multi-temporal 3D models. For historical 3D model data especially, it is no longer possible to supplement surveying GCPs. The goal of this paper was to complete the geometric registration of multi-period 3D models with a uniform spatial reference.