With the continuous development of industrial automation, the three-dimensional reconstruction technology of workpieces is playing an increasingly important role in the manufacturing industry. In actual working environments, there is a common problem of stacking workpieces, which significantly impacts subsequent work including robot recognition and grasping. Currently, it is hard for 3D reconstruction to extract image feature points and achieve accurate feature registration in workpieces with weak textures. To address the above issues, this study proposes a 3D reconstruction method for stacked workpieces based on deep learning with multi-view stereo matching. Firstly, multiple images from different perspectives are input through a DCNv2-based feature pyramid network for feature extraction. Then, homography transformation is performed to construct cost volumes, and a unified cost volume is obtained through variance aggregation. In the regularization section of the cost volume, an SE channel attention module is introduced to improve the feature expression ability of the network and enhance the performance and generalization ability of the model. This method exhibits good performance on the Danish Technical University (DTU) dataset. The point cloud model of stacked workpieces generated by this method is of great significance for future applications of industrial automation.