A great deal of research has demonstrated recently that multi-view stereo
(MVS) matching can be solved with deep learning methods. However, these efforts
were focused on close-range objects and only a very few of the deep
learning-based methods were specifically designed for large-scale 3D urban
reconstruction due to the lack of multi-view aerial image benchmarks. In this
paper, we present a synthetic aerial dataset, called the WHU dataset, we
created for MVS tasks, which, to our knowledge, is the first large-scale
multi-view aerial dataset. It was generated from a highly accurate 3D digital
surface model produced from thousands of real aerial images with precise camera
parameters. We also introduce in this paper a novel network, called RED-Net,
for wide-range depth inference, which we developed from a recurrent
encoder-decoder structure to regularize cost maps across depths and a 2D fully
convolutional network as framework. RED-Net's low memory requirements and high
performance make it suitable for large-scale and highly accurate 3D Earth
surface reconstruction. Our experiments confirmed that not only did our method
exceed the current state-of-the-art MVS methods by more than 50% mean absolute
error (MAE) with less memory and computational cost, but its efficiency as
well. It outperformed one of the best commercial software programs based on
conventional methods, improving their efficiency 16 times over. Moreover, we
proved that our RED-Net model pre-trained on the synthetic WHU dataset can be
efficiently transferred to very different multi-view aerial image datasets
without any fine-tuning. Dataset are available at this http URL

本研究提出了一个名为 WHU 的合成航空数据集，该数据集是首个大规模的多视角航空数据集，并介绍了一种名为 RED-Net 的新型神经网络，专门用于高精度的三维地球表面重建。通过实验，我们证实了该方法的性能和效率，在减少内存和计算成本的同时，使 MAE 提高了 50％以上，并且比传统方法的一些最佳商业软件的效率提高了 16 倍。