Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis

Abstract

Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike images that have a Euclidean structure, 3D point clouds are irregular since the neighbors of each node are inconsistent. Many studies have tried to develop various convolutional graph neural networks to overcome this problem and to achieve great results. Nevertheless, these studies simply took the centroid point and its corresponding neighbors as the graph structure, thus ignoring the structural information. In this paper, an Affinity-Point Graph Convolutional Network (AP-GCN) is proposed to learn the graph structure for each reference point. In this method, the affinity between points is first defined using the feature of each point feature. Then, a graph with affinity information is built. After that, the edge-conditioned convolution is performed between the graph vertices and edges to obtain stronger neighborhood information. Finally, the learned information is used for recognition and segmentation tasks. Comprehensive experiments demonstrate that AP-GCN learned much more reasonable features and achieved significant improvements in 3D computer vision tasks such as object classification and segmentation.

An efficient method for producing deep learning point cloud datasets based on BIM 3D model and computer simulation

“…In recent years, 3D deep learning based on point cloud data has become one of the most important research hotspots in the field of computer vision, and the core research directions include classification of point cloud data [1][2], semantic segmentation [3][4], instance segmentation [5][6], and target detection [7][8], etc. The research results have been applied to robotics and autonomous driving, industrial vision, 3D reconstruction [9][10][11][12], etc., and also have greater potential in other fields such as engineering construction, urban operation, and structure detection [13][14].…”