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EI-MVSNet:具有间隔感知标签的双极导向多视图立体网络.

Jiahao Chang, Jianfeng He, Tianzhu Zhang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 9, 2024
    PubMed
    概括

    本研究介绍了EI-MVSNet,这是一种新的多视图立体 (MVS) 网络,通过使用极导向卷积来对齐视图中的特征来提高深度估计. 该方法在标准基准上取得了最先进的结果.

    科学领域:

    • 计算机视觉 计算机视觉
    • 这是一种摄影计量技术 (photogrammetry).
    • 机器学习 机器学习

    背景情况:

    • 基于学习的方法是有效的多视图立体声 (MVS) 深度估计.
    • 现有的方法在成本量构造过程中经常忽略受体场对齐.
    • 当前MVS网络的缺陷限制了性能.

    研究的目的:

    • 提出一个带有间隔感知标签的双极导向多视图立体网络 (EI-MVSNet).
    • 为了提高深度估计准确度和MVS的稳定性.
    • 解决现有的MVS方法在特征对齐和深度预测方面的局限性.

    主要方法:

    • 开发了一个以极导向的体积结构模块,使用极导向的卷积来对准受体场,并考虑旋转/尺度变化.
    • 实施了间隔感知深度估计模块,用于直接成本体积监督,使用上下边界进行细粒度预测.
    • 将这些模块集成到统一的MVS架构中.

    主要成果:

    • 在MVS任务上,EI-MVSNet实现了最先进的性能.
    • 该方法在坦克和寺基准的中间和高级子集中排名第一.
    • 与现有的MVS方法相比,证明了高精度和强大的稳定性.

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    结论:

    • 拟议的EI-MVSNet有效地改善了多视图立体重建中的深度估计.
    • 通过极导向的体积构造和间隔感知深度估计提高了网络的精度和稳定性.
    • EI-MVSNet代表了MVS技术的重大进步.