# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
## 5 Conclusions and Discussion  
As mining operations continue to evolve, the integration of advanced technologies becomes essential to improve efficiency, accuracy, and safety. This paper explores the application of latent diffusion models to address these challenges, providing new insights into ore block perception and reconstruction. Below, we summarize the key contributions, findings and future outlook of this research:  
1. In future mining scenarios, remote-controlled and autonomous shoveling are expected to become mainstream methods. To obtain more comprehensive information about ore blocks from limited viewpoints, thereby enhancing the accuracy of perception for remote operators or autonomous machinery, the latent diffusion models are used for predicting new views of single-view ore blocks.
2. To improve the accuracy of the model in downstream tasks related to ore block view synthesis, a self-made ore block image-pose dataset created using simulation software is used for transfer learning. Additionally, introducing structural similarity loss constrains the predicted results during the training process, which helps to optimize details and improve consistency among generated views. The model performs well even when real images are used as input.
3. Combining applications in computer vision and mining engineering, the predicted multi-view images can be further used to generate a 3D model of the ore using neural surface reconstruction methods. This helps to represent the global information of the ore blocks more intuitively.
4. In shoveling scenarios, there are still some limitations in generating new views and performing 3D reconstruction of ore blocks based on global information perceived from limited viewpoints. These limitations include the high hardware resource requirements for running the model, the need for greater real-time performance in view synthesis and 3D reconstruction in actual shoveling scenarios, and the consistency between generated images that still requires improvement. However, future research  
Figure 8: An application process example for generating a 3D model based on multi-views of the ore blocks.  
can build on the current results to further explore the acquisition of physical information such as volume, surface texture, and roughness of the predicted ore blocks through 3D models, thereby enriching the diversity of ore block information and enhancing the completeness of the prediction tasks.  
L.B.: formal analysis, funding acquisition, supervision, writing--review and editing; D.B.: data curation, investigation, software, writing--original draft, writing--review and editing; B.C.: methodology, resources, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Key R&D Program of China, 2023 YFC2907305.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors gratefully acknowledge the funders and all advisors and colleagues who supported our work.  
Author [PERSON] was employed by the company Changsha Digital Mine Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.