# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
###### Abstract  
To address the problem of incomplete perception of limited viewpoints of ore blocks in future remote and intelligent shoveling-dominated mining scenarios, a method of using new view generation technology to predict ore blocks with limited view based on a latent diffusion model is proposed. Initially, an ore block image-pose dataset is created. Then, based on prior knowledge, the latent diffusion model undergoes transfer learning to develop an intelligent ore block shape prediction model (IOBSPM) for rock blocks. During training, structural similarity loss is innovatively introduced to constrain the prediction results and solve the issue of discontinuity in generated images. Finally, neural surface reconstruction is performed using the generated multi-view images of rock blocks to obtain a 3D model. Experimental results show that the prediction model, trained on the rock block dataset, produces better morphological and detail generation compared to the original model, with single-view generation time within 5 s. The average PSNR, SSIM, and LPIPS values reach 23.02 dB, 0.754, and 0.268, respectively. The generated views also demonstrate good performance in 3D reconstruction, highlighting significant implications for future research on remote and autonomous shoveling. global perception of ore blocks; latent diffusion model; structure similarity constraint; new view synthesis; 3D reconstruction; shovel loading +
Footnote †: journal: Applied sciences
neurons, processing and learning information through multiple layers of weighted neural connections. It is capable of adaptively optimizing weights using training data to generate predictions in complex pattern recognition and forecasting tasks. However, this method has limited transferability to other mining sites and requires substantial computational resources. [PERSON] et al. [6] used cubic spline interpolation, linear interpolation, nearest neighbor interpolation, and piecewise cubic Hermite interpolation to predict the ore block excavation volume per unit time in autonomous shoveling under different conditions. Nonetheless, the estimation results were affected by the smoothness of the operating site and surrounding environmental conditions. Thus, while traditional prediction algorithms have enhanced the richness of information to a certain extent, the limitations of various constraints have gradually become a problem that cannot be ignored. Currently, the new view synthesis method based on computer vision is less impacted by the surrounding environment and can more intuitively and accurately present multi-dimensional ore block information as images. In recent years, [PERSON] et al. [7] introduced neural radiance fields (NeRF), a technique that achieves realistic image generation from novel viewpoints. The method works by inputting 5D pose parameters (comprising 3D spatial coordinates and 2D viewing directions) of each sampled point into a multilayer perceptron [8]. However, the effectiveness of this method relies heavily on a large number of multi-view images and requires precise pose data [9], making it perform poorly with limited input [10]. Moreover, installing numerous sensors or camera equipment to capture these views would significantly increase costs. To address this, [PERSON] et al. [11] proposed a method incorporating convolutional neural networks to learn implicit geometric priors from the input image space for few-shot training. This approach enabled the synthesis of new views from a limited number of perspectives without requiring 3D supervision on the ShapeNet synthetic dataset [12]. Despite producing good view synthesis results, the method demonstrated poor view generation and generalization performance for non-ShapeNet synthetic datasets. Currently, the denoising diffusion probabilistic models (DDPM) [13] have shown promising results in the field of view synthesis. The model gradually adds noise to the data in the forward stage and restores the Gaussian noise to the original image in the reverse stage [14; 15].