3D (3-Dimensional) object recognition is a hot research topic that benefits environment
perception, disease diagnosis, and the mobile robot industry. Point clouds collected by
range sensors are a popular data structure to represent a 3D object model. This paper
proposed a 3D object recognition method named Dynamic Graph Convolutional Broad
Network (DGCB-Net) to realize feature extraction and 3D object recognition from the point
cloud. DGCB-Net adopts edge convolutional layers constructed by weight-shared multiple …

Abstract 3D (3-Dimensional) object recognition is a hot research topic that benefits
environment perception, disease diagnosis, and the mobile robot industry. Point clouds
collected by range sensors are a popular data structure to represent a 3D object model. This
paper proposed a 3D object recognition method named Dynamic Graph Convolutional
Broad Network (DGCB-Net) to realize feature extraction and 3D object recognition from the
point cloud. DGCB-Net adopts edge convolutional layers constructed by weight-shared …