LGCPNet : Local-global combined point-based network for shape segmentation. (June 2021) - Guan, Boliang; Li, Hanhui; Zhou, Fan; Lin, Shujin; Wang, Ruomei
Highlights: The irregularity and complexity of mesh data make shape segmentation challenging. Mesh data are similar to point cloud data in spatial property so shape segmentation can be treated as a pointwise labeling problem. Spatial information of mesh data is able to be learned by a point based network architecture by transforming shapes into point sets. A simple and effective shape segmentation method which treats mesh segmentation as point labeling by converting each face into its barycenter and normal vector per shape with the spatial information of given shape preserving. A intuitive and efficient operation, called Barycentric Dual Graph Edge Convolution (BDGEC), extracts edge features by operating over the nodes of Barycentric Dual Graph (BDG) intrinsic for the given shape. A novel point based DNN, named LGCP Net, is proposed for mesh segmentation which can capture local and global features of shape efficient to obtain satisfying segmentation performance. Graphical abstract: Abstract: Segmenting 3D shapes represented by meshes remains a challenging problem, due to the irregularity and complexity of meshes. Point cloud, on the other hand, can be considered as the simplest no-frills approximation for meshes. Therefore, in this paper, we regard the shape segmentation problem as a point labeling task: Given a shape, we first transform it into points encoding barycenters and normal vectors of faces. Then we construct a Barycentric Dual Graph (BDG) on the transformed points, and propose a Barycentric Dual Graph Edge Convolution (BDGEC) to extract features from the graph. Based on the BDGEC, we further propose a novel point-based deep neural network (DNN) named local-global combined point-based network (LGCPNet). Our LGCPNet consists of three modules, of which the Local Module and Global Module capture local and global features respectively, while the Fusion Module uses a gate mechanism to aggregate local features and global features, and obtain the point labeling result. Comprehensive experimental results on various datasets demonstrate that the proposed network inherits the merits of point-based DNNs and achieves the state-of-the-art performance.