## 1 Introduction
Studies on slope displacement measurement based on UAV photogrammetric point clouds have been carried out for some researches. An analytical method was explored that concluded a geometrical relationship among camera, measurement points and photographed images which made it possible to monitor the slope displacement by analyzing UAV photogrammetric point clouds [15]. The binocular vision system was used in the displacement monitoring for slope stability evaluation, which is convenient and accurate [16]. These studies supply a possibility for using UAV photography to analyze the slope displacement. On UAV photogrammetric measuring, a way of image recognition [17] based on texture and tested one hundred UAV images to validate the correct classification rate was proposed. Based on the correct classification, the reliability of UAV for landslide body imaging detection and displacement analysis was proved by collecting real local landslide statistics [18]. [PERSON] et al. used UAV to collect images for many times, generated digital elevation model and analyzed the surface of two landslides at a certain time interval to calculate the displacement of landslide in that time period [19]. Besides, [PERSON] et al. used UAV collecting video data and analyzed every picture to measure the civil infrastructure displacement such as bridges [20]. All the research provides a way of monitoring displacement by UAV images, however, the distortion was caused by camera lens that affect the accuracy of displacement analysis. It is of necessity to correct distortion before using the UAV images to work on a better accuracy level. However, due to the limitation of camera resolution and flight height, this method still cannot meet the requirements of slope displacement measurement accuracy. At present, the scholars abroad and at home mostly focus on proposing new registration method and improving the registration efficiency. On the method proposition, [PERSON] forwarded a method of registration for 3D shapes [21] including point sets, curves and surfaces, it built the fundamental in registration field. Furthermore, fully automatic registration of 3D point clouds is proposed which includes coarse alignment based on extended Gaussian image and fine alignment based on ICP algorithm [22]. Based on the existing research, the image-based registration method for point clouds was also proposed. [PERSON] et al. proposed registration method using UAV images and LiDAR data generating 3D model [23]. Currently, [PERSON] et al. concluded a new automated registration method by using sphere target to combine multi-view point clouds and developed a system to automatize the processing flow [24]. The time consumption of registration is also vital for ensuring the efficiency. [PERSON] put forward an improvement [25] for two steps of ICP [26] with optimal neighborhood knowledge and successfully applied it in ALS, TLS and MLS. [PERSON] et al. proposed an algorithm combining genetic algorithm with ICP, which registers the TLS and MLS, and improved the efficiency of traditional GA registration method [27]. To solve the problem of high time consumption, a new point clouds automatic registration algorithm based on PCA algorithm and ICP algorithm is proposed [28]. There are adequate basic researches on point clouds registration, but most of them were concentrated on two entire point clouds registration which may consume much more time, so generating partial point clouds as complement to make up the view-blocked area is a more efficient way that proposed in this paper. Generating point clouds by using UAV has some advantages that includes easy-operation, low hardware-cost and dense point clouds compare with generation cloud points by ALS [29], and can increase the point clouds density then improves accuracy in a certain way. In this study, an improved point clouds model for displacement assessment of slope surface by combining TLS and UAV photogrammetry was used for the first time to calculate high slope surface displacement at a highway high slope in Yunnan Province.