To address the challenges of long reconstruction time and numerous model voids in large-scale scenes and weakly textured regions during 3D reconstruction of unmanned aerial vehicle (UAV) images using existing multi-view stereo reconstruction (MVS) algorithms, an improved 3D reconstruction algorithm based on PatchMatch MVS (PM-MVS), called MCP-MVS, is proposed. The algorithm employs a multi-constraint matching cost computation method to eliminate outlier points from the 3D point cloud, thereby enhancing robustness. A pyramid red-and-black checkerboard sampling propagation strategy is introduced to extract geometric features across different scale spaces, while graphics processing unit based parallel propagation is exploited to improve the reconstruction efficiency. Experiments conducted on three UAV datasets demonstrate that MCP-MVS improves reconstruction efficiency by at least 16.6% compared to state-of-the-art algorithms, including PMVS, Colmap, OpenMVS, and 3DGS. Moreover, on the Cadastre dataset, the overall error is reduced by 35.7%, 20.3%, 19.5%, and 11.6% compared to PMVS, Colmap, OpenMVS, and 3DGS, respectively. The proposed algorithm also achieves the highest F-scores on the Cadastre and GDS datasets, 75.76% and 79.02%, respectively. These results demonstrate that the proposed algorithm significantly reduces model voids, validating its effectiveness and practicality.