Abstract: Aiming at the difficulty of accurate mapping between 2D images and 3D shapes during single-view 3D reconstruction of complex topological objects, a novel single-view 3D reconstruction method combining convolution Catmull-Rom spline free-form deformation (CR-FFD) with offset transformer capsule network is proposed. Firstly, the control points of the 3D point cloud model are interpolated by the basis functions of Catmull-Rom spline to maintain the consistency of the local topological structure during the deformation process. Then, the least square method based on convolutional neural network is proposed to accelerate the calculation process through nonlinear parameter mapping. Finally, an offset attention Transformer capsule network is designed to enhance the local feature expression ability and capture the fine-grained features of point cloud shape. Experiments on ShapeNet dataset show that the average values of EMD and CD are 3.84 and 3.71, respectively. The average values of EMD and CD on Pix3D dataset are 5.51 and 5.39, respectively. Compared with the existing single-view point cloud 3D reconstruction methods, the proposed method can effectively improve the quality of single-view 3D reconstruction and maintain the consistency of reconstruction from different angles.