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To solve the problem of insufficient information extraction in deep learning point cloud classification networks due to the rapid increase in dimension, as well as the issue of channel information balance leading to a lack of better discrimination ability for object features, we propose a point cloud classification network with steadily increasing feature dimensions and attention mechanism. By transforming multiple combinations of channel numbers and layers, the optimal combination of channel numbers and layers in the PointNet feature extraction module was determined, and the channel numbers were changed from (64, 64, 64, 128, 1024) to (32, 64, 128, 256, 512, 1024), resulting in a network with a feature dimension that increases steadily by a factor of 2.Incorporate the SENet channel attention mechanism into the evenly dimensionality-increased network, which weights the feature channels to enable the network to identify more accurate features and thus improve the discriminative power of object features. After training and testing on the ModelNet40 dataset, the improved classification network achieved an overall accuracy improvement of 2.58% compared to the PointNet classification network.
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