Point clouds feature frequency domain analysis based on multilayer perceptron

We propose a method for frequency domain feature analysis of point clouds based on the multilayer perceptron paradigm (MLP), named PFFA-MLP. By leveraging the Fourier transform and the latest MLP as a replacement for the transformer mechanism, PFFA-MLP maps point clouds to the frequency domain using a three-dimensional (3D) discrete Fourier transform. PFFA-MLP employs lightweight stacked DGCNN modules for initial feature extraction on multi-scale point cloud signals. It also includes a frequency domain feature analysis module, based on the MLP, which analyzes and aggregates the extracted multi-scale point cloud features. Point cloud understanding experiments are conducted on the ModelNet40, ScanObjectNN, and ShapeNet Part benchmarks. On ModelNet40, PFFA-MLP achieves an accuracy of 98% compared to PointMLP, while improving the detection speed by 7.2 times. On ScanObjectNN, the accuracy reaches 94% of PointMLP, while the detection speed is improved by 3.2 times. On ShapeNet Part, it achieves 97% of PointMLP. The experimental results demonstrate that PFFA-MLP achieves a favorable trade-off between training and inference speed. This method is applicable in scenarios that require efficient point cloud analysis while maintaining remarkable detection precision.