3D mixed CNNs with edge-point feature learning

Although deep neural networks have shown good performance on grid data, it is challenging to design deep neural networks for point cloud processing due to the irregular domain and disordering of point cloud. This paper presents a novel convolution block named edge-point features mixed convolution (EPFM-Conv) which effectively integrates the graph-based method and the point-based strategy. The entire EPFM-Conv block constructs the edge branch and point branch to extract the local and global features of point cloud, respectively. In the edge branch, we first construct the local dynamic graphs on point cloud and thereby extract rich detailed features through edge convolution. Next, the features of the edge are further abstracted and aggregated through the grouped residual learning followed by a symmetric function. In the point branch, we directly extract the features of the point by a point-wise multi-layer perceptron. Finally, the features of edge and point are mixed adaptively to obtain the feature representation of point cloud. Compared with the existing methods, the main difference is that the proposed method dexterously constructs the local dynamic graphs on point cloud for extracting rich detailed features, and synchronously designs the edge and point branches to extract the local and global features. Meanwhile, the proposed method uses the grouped residual learning to improve the information flow, thereby effectively deepening the depth of the network. Extensive experiments demonstrate that the proposed EPFM-Conv achieves the excellent performances on shape classification as well as part segmentation tasks. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel convolution block EPFM-Conv, which effectively integrates graph-based method and point-based strategy for extracting local and global features of point cloud. By constructing dynamic graphs and designing edge and point branches, rich detailed features are extracted, while grouped residual learning is used to deepen the network.