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    科学领域:

    • 医疗成像医学成像
    • 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

    背景情况:

    • 在内镜中精确的3D测量对于癌症诊断,计算机辅助干预和训练深度神经网络至关重要.
    • 带有微型图案投影仪的活跃立体声系统为内镜3D测量提供了一个有前途的方法.
    • 现有的多优化方法依赖于精确的初始3D三角网格,这限制了它们在现实世界内镜系统中的实用性.

    研究的目的:

    • 开发一种新的3D场景表示和优化方法,用于内镜主动立体声系统.
    • 为了克服现有方法中对准确初始3D三角形网格的依赖.
    • 为了提高3D内镜测量的准确性和稳定性.

    主要方法:

    • 神经辐射场 (NeRF) 适应在主动立体内镜框架内的3D场景表示.
    • 使用多数据同时优化3D场景和摄像机/投影机姿势.
    • 集成差分染以优化姿势和场景,而不需要初始的3D网格.

    主要成果:

    • 使用合成和真实内镜图像进行成功的3D重建.
    • 证明了3D场景和相机/投影机姿势的同时优化.
    • 消除了对准确的初始3D三角网的需求,提高了实际应用性.

    结论:

    • 拟议的基于NeRF的方法可以实现准确和强大的3D内镜测量,而不依赖初始形状信息.
    • 这种方法显著提高了活跃立体声系统的实用性,用于癌症诊断和人工智能培训数据生成等临床应用.
    • 一次性内镜深度信息测量是各种医疗应用的可行解决方案.