RCE-Net: A Novel Multiscale Attention Network for Large-Scale Aerial 3-D Reconstruction

Recent studies demonstrate deep learning’s effectiveness for multi-view stereo (MVS) applications. However, most existing approaches focus primarily on close-range objects, with limited dedicated solutions for large-scale urban reconstruction. We present RCE-Net, a novel network specifically designed for large-area aerial imagery. Our method enhances multi-scale feature perception through an efficient attention module, addressing traditional convolutional neural networks’ limitations in complex scene feature learning. The proposed architecture incorporates both channel and spatial attention mechanisms during cost volume regularization. This dual attention approach significantly improves the network’s ability to discriminate between local and global features. Experimental results show our method achieves excellent performance across varying resolutions of the WHU dataset.