Single-view 3D object reconstruction based on NFFD and graph convolution

To address the issue of inaccurate single-view three-dimensional (3D) object reconstruction results caused by complex topological objects and the absence of irregular surface details, a novel single-view 3D object reconstruction method combining non-uniform rational B-spline free deformation with a graph convolution neural network is proposed. First, a control points generation network, which introduces the connection weight policy, is used for the feature learning of two-dimensional views to obtain their control points topology. Subsequently, the NURBS basis function is used to establish the deformation relationship between the vertex contours of the point cloud model. Finally, to enhance the details, a convolutional network embedded with a mixed attention module is used to adjust the position of the deformed point cloud to reconstruct complex topological structures and irregular surfaces efficiently. Experiments on ShapeNet data show that the average values of the CD and EMD indices are 3.79 and 3.94, respectively, and that good reconstruction is achieved on the Pix3D real scene dataset. In contrast to existing single view point cloud 3D reconstruction methods, the proposed method offers a higher reconstruction accuracy of 3D objects and demonstrates higher robustness. © 2022, Science Press. All right reserved.