通过点云-BEV特征融合与星级编码进行动态模具造场景语义细分
Xuewen Feng1,2, Aiming Wang1, Guoying Meng1
1School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的3D点云和鸟视图 (BEV) 语义细分框架,用于子模拟造. 该方法提高了捕获工件变形的准确性,并提高了智能制造的实时效率.
科学领域:
- 计算机视觉 计算机视觉
- 制造业 制造技术 制造技术
- 人工智能的人工智能
背景情况:
- 3D点云的语义细分对于子模具造的智能监控至关重要.
- 现有的方法与变形特征作斗争,并提出造环境中常见的变化.
- 当前最先进的 (SOTA) 方法缺乏针对造特定挑战的专业设计,影响准确性和实时性能.
研究的目的:
- 为复杂的模具造场景开发一种新的语义细分框架.
- 解决捕捉细粒度变形和多尺度特征不对齐方面的局限性.
- 为了提高实际造应用的细分精度和实时效率.
主要方法:
- 一个融合3D点云和鸟视图 (BEV) 表示的框架.
- 在BEV编码中使用基于Star的编码模块来捕获工件变形.
- 层次的特征补偿对齐和加权的自适应融合,以实现交叉模式的交互和精度.
主要成果:
- 拟议的方法在经过模拟数据培训后直接在真实数据上测试时,比RPVNet高出1.1%的mIoU.
- 使用最小的真实数据进行微调进一步提高了mIoU的5%,显示出显著的性能增长.
- 该框架有效地处理细粒度变形和多尺度特征不对齐.
结论:
- 新的融合框架显著推进了子压的语义细分.
- 该方法在模拟和现实造场景中展示了强大的性能和适应性.
- 这种方法为造行业的智能流程监控和质量控制提供了实际解决方案.
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