Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/18/8273 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850259040926433280 |
|---|---|
| author | Lin Bi Dewei Bai Boxun Chen |
| author_facet | Lin Bi Dewei Bai Boxun Chen |
| author_sort | Lin Bi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e4b9643ca0de4671b47b379742a4dd49 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e4b9643ca0de4671b47b379742a4dd492025-08-20T01:55:58ZengMDPI AGApplied Sciences2076-34172024-09-011418827310.3390/app14188273Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis TechnologyLin Bi0Dewei Bai1Boxun Chen2School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaTo 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.https://www.mdpi.com/2076-3417/14/18/8273global perception of ore blockslatent diffusion modelstructure similarity constraintnew view synthesis3D reconstructionshovel loading |
| spellingShingle | Lin Bi Dewei Bai Boxun Chen Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology Applied Sciences global perception of ore blocks latent diffusion model structure similarity constraint new view synthesis 3D reconstruction shovel loading |
| title | Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology |
| title_full | Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology |
| title_fullStr | Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology |
| title_full_unstemmed | Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology |
| title_short | Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology |
| title_sort | intelligent prediction of ore block shapes based on novel view synthesis technology |
| topic | global perception of ore blocks latent diffusion model structure similarity constraint new view synthesis 3D reconstruction shovel loading |
| url | https://www.mdpi.com/2076-3417/14/18/8273 |
| work_keys_str_mv | AT linbi intelligentpredictionoforeblockshapesbasedonnovelviewsynthesistechnology AT deweibai intelligentpredictionoforeblockshapesbasedonnovelviewsynthesistechnology AT boxunchen intelligentpredictionoforeblockshapesbasedonnovelviewsynthesistechnology |