Abstract: Detection of compaction quality of rockfill dams is one of the important safeguards for the safe and stable operation of dam projects, and the measurement of the detection pit volume in the traditional irrigation method is time-consuming and labor-intensive, and the use of three-dimensional laser scanning technology can quickly calculate the detection pit volume and improve the efficiency of compaction quality detection, and the accurate extraction of the detection pit point cloud data from the scanned point cloud data is the key link to ensure the calculation accuracy of the method. In this paper, a point cloud extraction method based on point cloud slicing is proposed: firstly, the point cloud data are sliced, then a one-way search sorting method is adopted to sort the points of each sliced point set, and finally a cluster segmentation method based on the sorting number of the sliced point set is adopted to segment each point cloud slice, so as to accurately extract the point cloud on the surface of the inspection pit. MATLAB programming is used to realize the rapid extraction of the point cloud of the rock pile dam inspection pit based on the point cloud slice segmentation, and the test is carried out by the point cloud data of the rock pile dam inspection pit to verify the effectiveness of the method.