## 4 Discussion  
In the complex environments of various engineering projects, there are often many slopes of high and steep gradients formed either naturally or by human activity, making it impossible for geological survey personnel to conduct investigations using traditional contact measurement methods. This paper focuses on high and steep highway slopes, utilizing non-contact measurement methods to obtain 3D point cloud data from the study area. Based on this 3D point cloud data, we propose a method for rock mass structural plane identification using Hough space transformation and normal vector interpolation region growing, along with a CFSFDP clustering method for rock mass structural planes. This method is applied to a regular assembly model to verify its effectiveness. Below are three points of discussion regarding the research methods and results:  
This paper employs the Hough space transform and vector interpolation region growing algorithm for rock mass structural plane identification and information extraction. The algorithm consists of two parts: Hough space transform for point cloud normal vector calculation and vector interpolation region growing. Both the Hough space transform and PCA methods calculate normal vectors based on neighborhood point relationships. PCA treats all points within a neighborhood as a whole, constructs a covariance matrix, performs eigenvalue decomposition, and finds the corresponding eigenvectors. The eigenvector with the largest variance represents the normal vector of the core point. When the core point is near or on the edge of a structural plane, points from non-core surfaces may be included in the covariance matrix, leading to inaccurate normal vector calculations. The core idea of the Hough space transform is to randomly select two points within the neighborhood to form a plane with the core point and calculate its normal vector. The normal vectors are stored in an accumulator container through a voting process. To ensure computational speed, a maximum number of planes is set; once this number is reached, it is assumed that sufficient confidence is obtained to ensure the accuracy of the point cloud normal vector. The normal vector with the highest number of votes is selected as the core point normal vector. This approach reduces the impact of points from non-core surfaces, making the Hough space transform method robust to noise and accurate in calculating the normal vectors at the edges of rock mass structural planes. In the cube point cloud normal vector visualization analysis experiment, the normal vectors calculated using the Hough space transform were perpendicular to the planes on both sides of the edges, showing a clear trend in normal vector changes. This demonstrates that the method has distinct advantages in calculating normal vectors for complex rock mass surface point clouds. Region growing, which only uses normal vector differences as the growth criterion, exhibits significant randomness. In practical applications, it is also evident that the identification of non-planar rock mass structural planes or complex areas of rock surfaces shows a decline in performance. To address this issue, future research could explore other descriptors that can represent the spatial characteristics of point clouds. Establishing a multi-dimensional feature fusion mechanism could replace the single-feature criterion with multi-dimensional feature values as criteria for region growing, thereby enhancing the effectiveness of rock mass structural planes identification. The Fast Search and Find of Density Peaks (CFSFDP) algorithm is simple in steps, can automatically find clustering centers and determine the number of clusters, and does not rely on complex parameter tuning. Although the overall efficiency is high, calculating the clustering distance \(d_{dist}(n_{i},n_{j})\) remains complex. Direct clustering of massive point cloud data requires calculating the clustering distance between each point and every other point,