# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 2 Related Works  
### Computation Methods Based on the Image Modality  
Currently, researchers mostly use traditional image processing methods to calculate ore rock fragment sizes. [PERSON] et al. [3] proposed a method for rock image segmentation using mean shift and watershed transformation to estimate block dimensions. [PERSON] et al. [4] introduced a threshold segmentation technique based on [PERSON]'s discriminant bidirectional window, which exhibited good edge preservation and noise resistance in ore rock images. [PERSON] et al. [5] utilized an improved watershed algorithm for rock segmentation and computed the distribution of rock block sizes by analyzing the projected diameters of mineral particles. [PERSON] et al. [6] proposed an adaptive watershed segmentation algorithm based on the shape of rock blocks, which solved the segmentation errors caused by adhesion and edge blur in images of blasting rock piles. [PERSON] et al. [7] simplified the process by computing the gradient map of rock images using the Sobel operator and extracting rock block contours through image binarization. By analyzing the size distribution of rock blocks, they established a correlation between blasting parameters and the degree of block fragmentation. [PERSON] et al. [8] utilized the Canny edge detection operator and  
Figure 1: Typical ore rocks formed after mining blasting: (**a**) Mining blasting operation; (**b**) Typical ore rocks.  
moment-preserving method to extract the edges of rock block images, achieving precise block segmentation and size calculation. In order to address the weak edge preservation problem in rock images caused by the Otsu thresholding method [9], [PERSON] et al. [10] introduced an upgraded strategy. They applied the dual-neighborhood technique to select the minimum threshold for image binarization, thereby enhancing the preservation capability of weak edges in rock blocks.  
In recent years, there has been an increasing use of neural network-based image processing methods by researchers to address the calculation of rock fragment sizes. [PERSON] et al. [11] utilized Fourier transform, Gabor, wavelet, and their combinations to extract features from rock images. These features were then fed as inputs to a neural network for computing the size distribution of rock fragments. [PERSON] et al. [12] presented four models that employed gradient enhancement, support vector machine, Gaussian process, and artificial neural network methods to calculate the size distribution of rock fragments. Their main objective was to optimize blasting parameters and enhance the efficiency of open-pit mining operations. [PERSON] et al. [13] introduced a deep learning-based method for rock image segmentation, utilizing annotated datasets to train an overall nested boundary detection model for segmenting rock blocks. [PERSON] et al. [14] applied image processing techniques to acquire the surface of rock blocks and built a neural network model to analyze the distribution of block sizes. [PERSON] et al. [15] constructed a convolutional neural network based on Mask R-CNN [16] for predicting the size of rock fragments. [PERSON] et al. [17] combined the residual structure of convolutional neural networks with the DUNet model [18] to propose a rock image segmentation model called RDU-Net. This model can dynamically adjust the receptive field based on the size and shape of different rock fragments. These features enable the accurate capture of the edges of rocks of varying sizes and shapes, leading to effective segmentation of rock images.