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Yi Cai1, Mingyu Guo1, Congying Wang1

  • 1Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen Key Lab of Micro-Nano Photonic Information Technology, State Key Laboratory of Radio Frequency Heterogeneous Integration, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.

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概括
此摘要是机器生成的。

一个新的变压器网络TTFDNet从单边纹图案中实现了非常准确的3D深度估计. 这种方法在制造业和计算机视觉领域的应用中以精度和稳定性脱而出.

关键词:
深度学习是一种深度学习.深度估计估计的估计.边缘投影的概况测量方法转移学习转移学习

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科学领域:

  • 计算机视觉 计算机视觉
  • 计量学 计量学 计量学
  • 机器学习 机器学习

背景情况:

  • 边缘投射造型测量对于3D形状测量至关重要.
  • 从单一边缘图案准确估计深度仍然是一个挑战.
  • 现有的方法在动态场景中往往缺乏精度或稳定性.

研究的目的:

  • 引入TTFDNet,这是一个基于变压器的网络,用于端到端的深度估计.
  • 为了提高单边缘图案分析的准确性和稳定性.
  • 为了实现制造和自动化中的实际应用.

主要方法:

  • 开发了TTFDNet,其中包括一个精确的轮和粗深 (PCCD) 前处理器,一个全球多维融合 (GMDF) 模块和一个渐进式深度提取器 (PDE).
  • 利用转移学习与边缘结构一致性评估 (FSCE) 来提高有限数据集的性能.
  • 对208个场景的网络进行了评估,以确定深度估计的准确性.

主要成果:

  • TTFDNet实现了0.00372毫米的平均绝对误差 (MAE),显著超过了Unet,PDE和PCTNet.
  • 证明了高精度,球的偏差为~90微米,金属部件的偏差为~6微米.
  • 在动态重建和各种成像条件下展示了出色的概括性和稳定性.

结论:

  • TTFDNet提供了一个最先进的解决方案,用于准确和强大的单深度估计.
  • 拟议的网络适用于制造,自动化和计算机视觉领域的现实应用.
  • 转移学习方法有效地利用变压器的能力,即使是小数据集.