Edge Enhancement Oriented Graph Convolutional Networks for Point Cloud Segmentation

Abstract

Aiming at the problem of poor edge effect segmentation in point cloud segmentation, which fails to fully utilize the correlation between the local geometric and semantic features of point cloud.We propose an edge-enhanced graph convolution point cloud segmentation model EdgeGCN-Net, aiming at further exploring the method of point cloud segmentation based on graph convolution neural network. The EdgeGCN-Net employs an enhanced adaptive graph convolutional operator (AdaptConv++) for feature extraction. It constructs semantic and edge branches for joint optimization, aiming to enhance the extraction and comprehension abilities of semantic information and edge features in point clouds. In addition, we propose a graph inference module called AGIM (Attention-based Graph Inference Module), which utilizes the attention mechanism and learnable adjacency matrices to enrich the global knowledge propagation of the channel graph, thus compensating for the loss of geometric information due to the superposition of network layers. Experimental results show that EdgeGCN-Net outperforms previous state-of-the-art segmentation models on both ShapeNetPart and S3DIS datasets with better segmentation performance and higher accuracy, demonstrating its excellent performance in point cloud segmentation tasks.

Keywords

• Dynamic graph convolution
• edge enhancement
• point cloud segmentation