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深度学习用于生成飞行时间摄像机文物.

Tobias Müller1, Tobias Schmähling1, Stefan Elser2

  • 1Institute for Photonic Systems Hochschule Ravensburg-Weingarten, University of Applied Sciences, Doggenriedstraße, 88250 Weingarten, Germany.

Journal of imaging
|October 25, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的基于学习的方法,使用MCW-Net从激光扫描生成现实的飞行时间 (ToF) 摄像头数据. 这种方法通过结合噪声模型来提高精度来增强传感器模拟.

关键词:
域名转移 域名转移 域名转移基于学习的模拟.飞行时间-飞行时间.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 传感器模拟传感器的模拟

背景情况:

  • 飞行时间 (ToF) 摄像机受到多路径干扰 (MPI) 噪音和错误的影响.
  • 获得足够的现实训练数据来纠正ToF错误是一个挑战.
  • 由于简化,现有的物理模拟数据往往缺乏关键传感器特性.

研究的目的:

  • 开发一种基于学习的方法来生成现实的ToF摄像头数据.
  • 为了克服ToF传感器开发当前模拟数据的局限性.
  • 为了提高ToF传感器模拟的准确性和适用性.

主要方法:

  • 利用高质量的激光扫描数据作为输入.
  • 使用MCW-Net (多级连接和广泛的区域非本地区块网络) 进行域名传输.
  • 集成噪声模型来模拟现实的传感器噪声.
  • 探索各种训练策略与现实世界的数据集.

主要成果:

  • 成功地将激光扫描数据转化为现实的ToF摄像头数据.
  • 证明了MCW-Net方法在域调整方面的有效性.
  • 与传统模拟相比,对参考场景的定量评估显示了比传统模拟更好的现实性.

结论:

  • 提出的基于学习的方法有效地产生高保真度的ToF摄像头数据.
  • 这种技术增强了模拟数据的实用性,用于ToF摄像机硬件设计和应用程序开发.
  • 该方法为ToF传感器研究中的数据稀缺提供了可行的解决方案.