To address the issues of poor completeness and low accuracy in 3D point cloud reconstruction of damaged aero-engine blades, a 3D reconstruction method based on the UCD-PatchmatchNet model is proposed. First, the UNet network is introduced to reconstruct the multi-scale feature extraction structure of the PatchmatchNet model, thereby enhancing its feature extraction capability. Second, the Convolutional Block Attention Module (CBAM) is employed to optimize the feature extraction module, enabling the model to focus on key regions and strengthening its feature extraction ability. Meanwhile, the randomly initialized depth map module is replaced with a Delaunay triangulation method based on depth gradient sampling to generate the initial depth map, improving the accuracy of the predicted depth map. Finally, validation is conducted on both public datasets and a self-established dataset of damaged aero-engine blades. The results show that, compared to the PatchmatchNet model, the proposed UCD-PatchmatchNet model reduces the accuracy error by 12.9%, the completeness error by 4.0%, and the overall error by 9.4%. The reconstructed point clouds of damaged blades exhibit good completeness and accuracy, better meeting the requirements for damage analysis of aero-engine blades.