Single-view 3D reconstruction is a challenging problem in computer vision. To improve the accuracy of the 3D model reconstructed by the existing 3D reconstruction algorithm, this study extracts both global and local features of the image. On this basis, the signed distance function (SDF) is used to describe the reconstructed 3D objects. In this way, high-quality 3D shapes are generated, and the model has higher accuracy and enhanced generalization capability, which enables the deep model to reconstruct other types of objects with high quality. Experiments demonstrate that compared with the most advanced reconstruction algorithm at present, the proposed deep network and the method for representing 3D shapes have better performance in the effects of reconstructed 3D models and the generalization of new objects.