Point Cloud Classification and Segmentation Network Based on Adaptive Feature Extraction

Point cloud classification and segmentation are key technologies for 3D perception and scene understanding, whose accuracy and efficiency directly affect the performance of high-level applications such as 3D modeling, object recognition, and intelligent interaction. Existing methods still exhibit obvious deficiencies in local feature representation, computational efficiency, and scene applicability. To address these issues, this paper proposes a lightweight point cloud classification and segmentation network based on adaptive feature extraction, referred to as AFE-PointNet. Firstly, an element-wise weighting set abstraction module based on the Hadamard product is designed. It leverages geometric topology learning to achieve adaptive feature enhancement, effectively improving the representation capability of local geometric structures. Meanwhile, a cascaded structure of feature aggregation and an inverted residual multi-layer perceptron (InvResMLP) is adopted for deep feature mining to achieve high-accuracy and high-efficiency point cloud classification and segmentation. Experimental results show that AFE-PointNet achieves an overall accuracy (OA) of 93.6% on the ModelNet40 dataset and 84.5% on the ScanObjectNN dataset, and attains a class mean intersection over union (Cls.mIoU) of 83.6% on the ShapeNetPart part segmentation dataset, yielding significant performance improvements over the PointNet++ model. The proposed adaptive feature enhancement and lightweight deep mining strategies effectively improve point cloud representation capability, providing a high-precision and efficient solution for 3D vision tasks.