Point cloud is a widely used geometric data structure in the missions of 3D reconstruction, digital city and geologic survey etc. Extracting sufficient information from the point cloud is the key to deal with the aforementioned missions. However, huge number of points lead to computational complexity and inefficiency during the training process. To deal with this problem, this paper proposes a novel framework name Principal Component Analysis Point Net (PCAPN) for the feature extraction of point cloud. Firstly, a sampling module namely Component Point Sampling (CPS) is designed for generating several candidate sets of points by different scales, which defines the centroids of local regions. Secondly, a feature extraction framework based on MLP structure is adopted for extracting the feature vectors from the points set generated from the sampling module. Finally, the extracted feature vectors are concatenated together, and then put into a fully connected layer for classification. The proposed framework was evaluated on 2 benchmarks, i.e. ShapeNet part data set and ModelNet40. The experimental results show that our framework is efficient and robust. In particular, the results are significantly better than those obtained by the state-of-the-art frameworks. Our network is 4.6% more accurate than PointNet and 1.1% higher than PointNet++.
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