基于边缘的结构光3D重建:原理,投影技术和深度学习集成
Zhongyuan Zhang1,2, Hao Wang1,2, Yiming Li2
1Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本综述比较了用于扩散表面的边缘投影形计 (FPP) 和用于镜面的相位测量偏移计 (PMD). 它强调了微电机系统 (MEMS) 和深度学习,用于先进的3D重建.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机视觉 计算机视觉
- 计量学 计量学 计量学
背景情况:
- 结构光3D重建是捕捉物体几何学的关键主动测量技术.
- 它被广泛用于工业检查,文化遗产和虚拟现实.
- 现有的审查往往缺乏主要的边缘基方法之间的系统比较,如边缘投影形计 (FPP) 和相位测量偏移计 (PMD).
研究的目的:
- 为主流边缘式3D重建方法提供全面的比较分析.
- 澄清不同投影方案 (例如数字光处理 (DLP),MEMS) 对系统性能的影响.
- 探索深度学习与FPP和PMD的整合,以提高准确性.
主要方法:
- 边缘投影形计 (FPP) 和相位测量偏移计 (PMD) 的系统比较.
- 分析测量原则,系统实施,校准和错误控制.
- 研究投影技术 (DLP,MEMS) 和深度学习集成.
主要成果:
- 在多个技术维度中比较FPP和PMD.
- 审查澄清了像DLP和MEMS这样的投影方案的影响.
- 深度学习显示了提高相位检索和3D重建精度的潜力.
结论:
- 微电机系统 (MEMS) 为轻量级,高动态范围的测量提供了潜力.
- 深度学习正在成为增强3D重建的关键工具.
- 未来的研究应该专注于系统建模,智能重建和性能评估.
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