基于新型视图合成技术的矿块形状智能预测技术

Q1 Mathematics Applied Sciences Pub Date : 2024-09-13 DOI:10.3390/app14188273
Lin Bi, Dewei Bai, Boxun Chen
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引用次数: 0

摘要

针对未来以远程智能铲运为主的采矿场景中矿块有限视角的不完全感知问题,提出了一种基于潜在扩散模型,利用新视角生成技术预测有限视角矿块的方法。首先,创建一个矿块图像姿态数据集。然后,基于先验知识,对潜在扩散模型进行迁移学习,建立岩块的智能矿块形状预测模型(IOBSPM)。在训练过程中,创新性地引入了结构相似性损失,以约束预测结果并解决生成图像的不连续性问题。最后,利用生成的岩块多视角图像进行神经曲面重构,得到三维模型。实验结果表明,与原始模型相比,在岩块数据集上训练的预测模型能生成更好的形态和细节,单视图生成时间不超过 5 秒,平均 PSNR、SSIM 和 LPIPS 值分别达到 23.02 dB、0.754 和 0.268。生成的视图在三维重建方面也表现出良好的性能,这对未来的远程和自主铲土研究具有重要意义。
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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, 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.
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来源期刊
Applied Sciences
Applied Sciences Mathematics-Applied Mathematics
CiteScore
6.40
自引率
0.00%
发文量
0
审稿时长
11 weeks
期刊介绍: APPS is an international journal. APPS covers a wide spectrum of pure and applied mathematics in science and technology, promoting especially papers presented at Carpato-Balkan meetings. The Editorial Board of APPS takes a very active role in selecting and refereeing papers, ensuring the best quality of contemporary mathematics and its applications. APPS is abstracted in Zentralblatt für Mathematik. The APPS journal uses Double blind peer review.
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