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Differential Graph Convolution Network for point cloud understanding
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DOI：10.1016/j.neucom.2024.127940.png)
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Abstract
En 中文
Smoothing of the graph convolution is not conducive to characterizing local differences of point cloud. To solve this problem, we propose a Differential Graph Convolutional Network (Differ-GCN) for point cloud analysis. First, we propose a new graph construction strategy that can make similar nodes in the local space belong to the same graph, which can better represent the local commonality. After that, the features of the graph are extracted by the similarity matrix. Some of the smoothing information of the graph is removed to optimize the over-smoothing nodes and combined with the local difference of the points to get the beneficial features for downstream tasks. Finally, each neighbor point is processed to generate a mask, and pooling is performed through the mask to reduce information loss. The experiment results show that Differ-GCN performs excellent in object classification and part segmentation. The processing speed of Differ-GCN for point cloud is much faster than the state-of-the-art methods.
Keywords:
Point cloud
Graph convolution
Graph construction
Over-smoothing
Differential information
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