Recognition and Segmentation of 3D Point Clouds Sensitive to Fusion Features

The 3D point cloud classification and segmentation networks ignore the redundant information in the fusion features, lack the ability to amplify the proportion of effective features, and cannot fully explore the expressiveness of features. Based on the CurveNet network, this paper proposes a method that can filter and enrich the fusion features, and the recognition and segmentation effect of point cloud reaches a relatively advanced level. Firstly, a feature selection subnetwork with filtering ability for fusion features is proposed, which combines TopK operator and a scoring mechanism to select fusion features containing valid information and adaptively assign weight to the selected features. Secondly, two new branches arc added to the aggregation curve feature module, so as to learn the curve internal point distance features and the curve line distance features, respectively, and extract the internal correlation of each branch through the quick channel affinity attention mechanism, which enhances the information description ability of the network features. The experimental results show that the accuracy of the classification task on the ModclNct40 datasct reaches 93. 8%, and the average intersection over union of the segmentation task on the ShapeNet Part datasct reaches 86. 4%. Compared with the benchmark network, the classification effect and segmentation effect are improved, which proves the effectiveness of the proposed algorithm. © 2024 Xi'an Jiaotong University. All rights reserved.