## 4 Research on Image Acquisition and Theoretical Methods
### Analysis and Processing of Point Cloud Data  
After obtaining the data for the 3D reconstruction of the borehole, it is essential to perform cutting point cloud filtering and denoising of the 3D model. This involves removing noise and irrelevant data points to enhance the quality and accuracy of the point cloud [52]. Additionally, curve fitting of the borehole point cloud projection data should be carried out based on the RANSAC (Random Sample Consensus) algorithm [53]. The RANSAC algorithm is particularly effective for fitting curves to data sets that contain a significant proportion of outliers. By iteratively selecting random subsets of the data and fitting a model to these subsets, RANSAC identifies the model that best represents the inlier data points, thereby ensuring a robust and accurate curve fitting. This process is crucial for accurately modeling the borehole's geometry and for further analysis and applications in geological studies and engineering projects [54]. Properly filtered and fitted point cloud data enables more precise measurements and a better understanding of the borehole characteristics.  
The RANSAC algorithm is a classical parameter estimation algorithm for estimating model parameters from datasets containing outliers. The basic idea is to find a model that fits the interior points best by random sampling and iteration. It has the advantage of being able to robustly estimate the model parameters and get better results even in the presence of a large number of outliers [55]. In order to reduce the computational effort, the RANSAC algorithm introduces a probability factor z and determines the number of iterations k based on the proportion of local interior points in the data set \(\omega\). The specific calculation method is:  
\[\mathrm{z}=1-(1-\omega^{\mathrm{n}})^{\mathrm{k}} \tag{11}\]  
\(1\omega^{\mathrm{n}}\) denotes the probability that none of the selections are all bureau points, and \((1\omega^{\mathrm{n}})^{\mathrm{k}}\) denotes the probability that none of the selections are all bureau points. Then there are:  
\[\mathrm{k}=\frac{\log(1-\mathrm{z})}{\log(1-\omega^{\mathrm{n}})} \tag{12}\]  
By setting the appropriate z and \(\omega\) (where z is generally required to satisfy greater than 95%), a reasonable number of iterations can be determined based on the characteristics of the data set k. This reduces the amount of computation while ensuring that a sufficient number of the n points selected are local points and improves the accuracy of the fitting results [56].  
After fitting the arc of the borehole, set a reasonable threshold to reject the arc of the borehole, and finally carry out the calculation of the halfhole rate and evaluate the blasting effect according to the evaluation criteria. The experimental flow chart is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline  & **2D Image** & **3D Point Cloud** \\ \hline form of expression & 2D projection of the 3D world & 3D data with z-value \\  & without z-values & \\ \hline data structure & Orderly, sequential links & Disorganization of data points \\ \hline Environmental sensitivity & Sensitive to ambient light & Insensitive to ambient light \\  & conditions & conditions \\ \hline Treatment & Direct use of CNN, etc. & Cannot use CNN directly \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of 2D image and 3D point cloud.