The latest advances in technical characteristics of unmanned aerial systems (UAS) and their onboard sensors opened the way for smart flying vehicles exploiting new application areas and allowing to perform missions seemed to be impossible before. One of these complicated tasks is the 3D reconstruction and monitoring of large-size, complex, grid-like structures as radio or television towers. Although image-based 3D survey contains a lot of visual and geometrical information useful for making preliminary conclusions on construction health, standard photogrammetric processing fails to perform dense and robust 3D reconstruction of complex large-size mesh structures. The main problem of such objects is repeated and self-occlusive similar elements resulting in false feature matching. This paper presents a method developed for an accurate Multi-View Stereo (MVS) dense 3D reconstruction of the Shukhov Radio Tower in Moscow (Russia) based on UAS photogrammetric survey. A key element for the successful image-based 3D reconstruction is the developed WireNetV2 neural network model for robust automatic semantic segmentation of wire structures. The proposed neural network provides high matching quality due to an accurate masking of the tower elements. The main contributions of the paper are: (1) a deep learning WireNetV2 convolutional neural network model that outperforms the state-of-the-art results of semantic segmentation on a dataset containing images of grid structures of complicated topology with repeated elements, holes, self-occlusions, thus providing robust grid structure masking and, as a result, accurate 3D reconstruction, (2) an advanced image-based pipeline aided by a neural network for the accurate 3D reconstruction of the large-size and complex grid structured, evaluated on UAS imagery of Shukhov radio tower in Moscow.

中文翻译：

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结合UAS图像和深度学习的复杂网格结构的3D重建

无人机系统（UAS）及其机载传感器的技术特征的最新进展为智能飞行器开发新的应用领域开辟了道路，并允许执行任务以前似乎是不可能的。这些复杂的任务之一是3D重建和监视大型，复杂，类似网格的结构，例如广播或电视塔。尽管基于图像的3D调查包含大量的视觉和几何信息，可用于就建筑健康做出初步结论，但标准的摄影测量处理无法对复杂的大型网格结构进行密集而健壮的3D重建。此类对象的主要问题是重复和自封闭的相似元素导致错误的特征匹配。本文介绍了一种基于UAS摄影测量技术对莫斯科（俄罗斯）舒霍夫无线电塔进行精确的多视图立体声（MVS）密集3D重建的方法。成功的基于图像的3D重建的关键要素是开发的WireNetV2神经网络模型，用于对导线结构进行可靠的自动语义分割。所提出的神经网络由于塔单元的精确遮罩而提供了高匹配质量。该论文的主要贡献是：（1）深度学习WireNetV2卷积神经网络模型在包含重复元素，孔，具有复杂拓扑结构的复杂网格结构图像的数据集上，胜过最新的语义分割结果自遮挡，从而提供强大的网格结构遮罩，并因此提供精确的3D重建，