## 4 Experiment and Discussion  
In order to verify the segmentation algorithm based on the registration result obtained by the G-Super4 PCS algorithm proposed by [PERSON] et.al [9], this paper also takes the columnar basalt data from Guabushan Geopark in Nanjing, China as the experimental data. The digital images are acquired by Fujifilm X-T10 digital camera, and the TLS point cloud is acquired by FARO Focus(tm) X330 terrestrial laser scanner. The test area is respectively shown as Figure 13a (the digital image) and Figure 13b (the point cloud). Figure 12: The updated RAG. Figure 13: The image of the geological research area. Firstly, the 1-\(N\) mapping relationship between the point cloud and images is obtained based on the registration result. According to mapping relationships between the TLS point cloud and different images, colors and textures of images could be added to the TLS point cloud. The correspondences of the TLS point cloud and images (a)-(g) in 3D visualization is shown as Figure 14.  
Secondly, distance measure model calculation is an important part of MFF-SLIC algorithm. For color feature calculation of digital image, RGB color space should be transformed into Lab color space first according to Section 3.2.1. There are 251,861 3D points in the experimental TLS point cloud. Calculate the local attitude of each 3D point in the point cloud according to Section 3.3.2. Figure 15a shows an intuitional distribution of local attitudes for 251,861 points (red points in the figure), which is a pole plot projected by equal angle. The range values of the dip angle and dip are respectively \([0^{\circ},90^{\circ}]\) and \([0^{\circ},360^{\circ}]\). The radius of the pole plot is \(90^{\circ}\), so the distance between the blue center of the circle and each red point denotes the dip angle of the red point, and correspondingly, its dip is equal to the angle between due North and the direction in which the red point is connected to the blue center of the circle. For example, dips angle and dips of two yellow cross marks in the pole plot are respectively \([65^{\circ},23^{\circ}]\) and \([40^{\circ},273^{\circ}]\)  
Figure 14: 3D visualization of the mapping relationship between TLS point cloud and images. Figure 15: (a) The pole plot of local attitudes for 251,861 points in the experimental TLS point cloud; (b) The contour plot of local attitudes for 251,861 points in the experimental TLS point cloud. Figure 15b shows the contour plot generated from local attitudes for 251,861 points. The contour plot describes poles density distribution characteristics and laws by use of isopleth based on the pole plot. The color gradations in Figure 15b represent fissure developmental state. In this paper, the overlapping area of Figure 14a is taken as an example for the following segmentation and clustering experiments of superpixels. In digital images, attitudes of those pixels corresponding to 3D points in the TLS point cloud could be obtained according to the 1-\(N\) mapping relationship between the two data sources. For those pixels without corresponding 3D points in the TLS point cloud, attitudes could be calculated via interpolation with Equation (24) in Section 3.2.3. The 2D visualization of attitudes for a digital image is shown as Figure 16.  
The next is to construct the multi-feature fusion distance measure model with all feature components of both the two data sources, and perform MFF-SLIC hybrid super pixel segmentation. The number of superpixels is a key parameter which affects the result of segmentation. This paper makes a comparison between 2D SLIC algorithm and MFF-SLIC algorithm by setting various superpixel number. Figure 17 shows the comparison result when the number of superpixels is set to 500. In this figure, the red and green lines respectively represent superpixel segmentation boundaries of the two algorithms.