The memory consumption issue in MVSNet reconstruction networks， compared with CVP-MVSNet and CasMVSNet networks， reduces memory usage when processing high-resolution images and improving the accuracy of reconstructed point clouds. However， both networks still exhibit significant errors in point cloud completeness. To address this issue， this paper proposes DA-MVSNet， a multi-view 3D reconstruction network based on dilated attention and depth optimal correction. DA-MVSNet uses CasMVSNet as the baseline network， with an additional feature enhancement network that integrates a parallel dilated convolution and attention module， incorporating the concept of depth-wise separable convolutions. This enhancement strengthens the network’s ability to capture global features of input views， improving point cloud completeness. To further enhance the accuracy of output depth maps and prevent the feature enhancement network from extracting irrelevant background information， which can degrade the accuracy of the reconstructed point cloud， an optimization correction mechanism based on nonlinear least squares is introduced at the output stage of the network. The results show DA-MVSNet reduces the accuracy and completeness errors of the reconstructed point cloud by 2.5% and 4.7%， respectively， on the indoor scene DTU dataset， achieving better overall performance. However， due to the additional feature enhancement network and correction mechanism， the memory and time consumption of DA-MVSNet are not very higher than those of CVP-MVSNet and CasMVSNet.