Abstract: Detection of compaction quality during rockfill dam construction is an important safeguard for its safe and stable operation, but the traditional irrigation method relies on measuring the detection pit volume that is time-consuming and labor-intensive. Today, the three-dimensional laser scanning technology can be adopted to calculate the detection pit volume quickly and improve the efficiency of compaction quality detection. To ensure the calculation accuracy of this new method, the key link is accurate extraction of the detection pit point cloud data from the scanned data. This paper develops a point cloud extraction method based on point cloud slicing that is able to accurately extract the point cloud on the surface of the inspection pit. First, the point cloud data is sliced; then a one-way search sorting method is used to sort the points of each sliced point set; finally, a cluster segmentation method based on the sorting number of the sliced point set is adopted to segment each slice. We have designed a MATLAB code to realize rapid extraction of the point cloud for a rockfill dam inspection pit based on this new method, and tested it using the point cloud data to verify its effectiveness and accuracy.

Key words: three-dimensional point cloud, rockfill dam, inspection pit, point cloud slicing, point cloud extraction