Simplified Algorithm of Geometric Model Region Segmentation Using Neural Network

Abstract

The simplification of three-dimensional (3D) models has always been a hot research topic for scholars. The researchers simplified different parts of the 3D point cloud data from both global and local information. Aiming at the need to retain detailed features in the simplification of 3D models, the neural network (NN) technology is firstly analyzed and studied, and a simplified algorithm for regional segmentation of geometric models based on Graph Convolutional Neural Network (GCNN) is proposed. Secondly, based on the idea of dense connection of DenseNet network structure, a symmetric segmentation model is established. The left part continuously performs Down-Sampling and local feature aggregation on the original geometric model through the Weighted Critical Points (WCPL) algorithm and edge convolution operation and performs compression encoding. At the same time, the right part uses the interpolation method for Up-Sampling the encoded data to increase the number of data points and feature dimensions, so as to restore the point cloud data to the dimensions before processing. Finally, it is restored to the dimension size of the original data to realize the end-to-end output of the segmentation model. Comparing the results with other segmentation models, it shows that (1) as the number of iterations increases, the regional accuracy of the training set increases; (2) after 1000 training rounds, from the perspective of the segmentation effect of a single category of objects, the model has good segmentation effect and has application prospects; and (3) compared with other models, the segmentation interaction ratio of the model is at a relatively mature level. The findings can provide a reference for the application of the segmentation technology of related geometric models and neural networks in the fields of similar models and image segmentation.