## 1 Introduction  
In rock mass structures, rock discontinuities occupy an extremely important position. Currently, the most commonly used method for investigating rock discontinuities is to use the geological compass and tape measure to obtain the orientation information of geological rock discontinuities, including tendency, dip angle, dip direction, spacing, density, and roughness. This method is simple with accurate results, but it is only applicable to measuring simple rock discontinuity surfaces that can be directly contacted, and its applicable scenarios are limited [1]. With the development of surveying technology, noncontact measurement are gradually being applied in geological surveys, such as photogrammetry [2, 3], 3D laser scanning technology [4, 5, 6], and underground television [7]. Among them, 3D laser scanning and UAV photogrammetry can obtain rock mass point cloud information quickly and accurately. Based on the obtained point cloud, K-means clustering [8], region growing [9], the Hough normal vector, and HSV [10] are used to process the point cloud, which can further achieve the intelligent identification of rock mass discontinuities, to dig out their detailed information such as spacing and roughness. Therefore, it has been favored by scholars. Some scholars have even made innovations in rock structural surface extraction algorithms. For example, [PERSON] et al. [11] used an improved region growing algorithm and analytic geometry theory to construct a 3D model of a point cloud and intelligently identify the rock mass orientation information. [PERSON] et al. [12] used the 3D laser scanning technology to obtain the rock mass point cloud information and intelligently identify the rock mass discontinuities by the improved Ransac algorithm and Graham Scan algorithm. [PERSON] et al. [13] used a double-nested mean-shift clustering algorithm and a region growing algorithm to identify and classify rock discontinuity surfaces to obtain geological information. [PERSON] et al. [14] proposed a rock mass discontinuity identification algorithm based on the sensitivity parameters (K-nearest-neighbor points, pinch angle threshold j, and filtering factor f) to rapidly identify rock mass discontinuities in the 3D point clouds. [PERSON] et al. [15] achieved the automatic identification of information of rock mass discontinuities based on the 3D point cloud data of the rock mass surface, through four steps of structural grouping analysis, grouping optimization, structural fitting, and coordinate system conversion. The reliability and accuracy of this method were verified by comparing with the field survey results of the slope rock discontinuities on the slope surface, which provided a reference method for the extraction of geometric information of rock discontinuities in 3D point clouds. In summary, using 3D laser scanning and photogrammetry is currently the most effective way to obtain a rock mass point cloud in the shortest time, but for special geographic situations, there are the following problems: first, when facing complex terrain, it is difficult to obtain complete point cloud information of rock masses by a single measurement method; second, when using different technical means to obtain multi-source point cloud data of rock masses, it is uncertain how to carry out multi-source point cloud fusion to ensure data integrity on the premise of data quality; finally, based on the multi-source point cloud information, how to accurately and quickly obtain the occurrence information of the rock mass is the focus and difficulty of the research, but there is very limited research in this area. Therefore, this paper takes a high and steep cliff on the south side of Laojun Peak in Huidong County, Sichuan Province as an example, and uses 3D laser scanning and UAV photogrammetry to collect data. Based on the multi-source fusion point cloud, the improved nearest-neighbor search algorithm combined with the coplanarity criterion is used to calculate the point cloud normal vector and HSV color rendering.