The rapid and accurate three-dimensional (3D) reconstruction of ocean waves holds paramount significance for marine engineering research. To address the issues of low processing efficiency in traditional ocean wave 3D reconstruction algorithms and the accuracy affected by too many holes during the generation of point clouds, this paper proposes an approach that combines disparity mask and self-supervised learning for 3D ocean wave reconstruction. First, the disparity images are obtained through training network model based on image reconstruction, disparity smoothness, and left-right disparity consistency losses. Second, a mask decoder is added to generate disparity mask images. Finally, through leveraging prior knowledge of common disparity regions, a novel mask loss function is designed to mitigate the impact of disparity noise in non-common regions and ocean surface occlusion problems. The experimental results on the Acqua Alta dataset demonstrate that the proposed method can reduce noise in ocean wave point clouds effectively. In the case of precision close to the traditional algorithm, the point cloud reconstruction speed reached 0.024 seconds per frame.