融合NFFD与图卷积的单视图三维物体重建

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

为了解决复杂拓扑结构及非规则表面细节缺失等导致的单视图三维物体重建结果不准确问题,本文提出了一种融合非均匀有理B样条自由形变(NFFD)与图卷积神经网络的三维物体重建方法。首先,通过引入连接权重策略的控制点生成网络对2D视图进行特征学习,获得其控制点拓扑结构。然后,利用NURBS基函数对控制点坐标自适应特性建立点云模型轮廓间顶点的形变关系。最后,为增强细节信息,将混合注意力模块嵌入图卷积网络对形变后的点云位置进行调整,从而实现复杂拓扑结构和非规则表面的高效重建。在ShapeNet数据集的实验表明,CD指标平均值为3.79,EMD指标平均值为3.94,并在Pix3D真实场景数据集上取得较好重建效果。与已有的单视图点云三维重建方法比较,本文方法有效地提高了重建精度,具有较强的鲁棒性。

To address the issue of inaccurate single-view three-dimensional(3D)object reconstruction re⁃sults 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 introduc⁃es 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 re⁃lationship between the vertex contours of the point cloud model.Finally,to enhance the details,a convo⁃lutional 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.