P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo

Learning-based methods are demonstrating their strong competitiveness in estimating depth for multi-view stereo reconstruction in recent years. Among them the approaches that generate cost volumes based on the plane-sweeping algorithm and then use them for feature matching have shown to be very prominent recently. The plane-sweep volumes are essentially anisotropic in depth and spatial directions, but they are often approximated by isotropic cost volumes in those methods, which could be detrimental. In this paper, we propose a new end-to-end deep learning network of P-MVSNet for multi-view stereo based on isotropic and anisotropic 3D convolutions. Our P-MVSNet consists of two core modules: a patch-wise aggregation module learns to aggregate the pixel-wise correspondence information of extracted features to generate a matching confidence volume, from which a hybrid 3D U-Net then infers a depth probability distribution and predicts the depth maps. We perform extensive experiments on the DTU and Tanks & Temples benchmark datasets, and the results show that the proposed P-MVSNet achieves the state-of-the-art performance over many existing methods on multi-view stereo.