Research on Partial Missing Reconstruction of 3D Point Cloud Model Based on Point Fractal Network

In order to solve the problem of partial loss of data information and structure of 3D point cloud model due to subjective and objective factors such as occlusion and noise, a partial deletion reconstruction method of 3D point cloud model based on PF-Net was proposed, and a model deletion reconstruction system was developed. Based on the deep learning PF-Net network architecture, the Batch Normal layer and the Dropout layer are introduced to normalize the original datasets in batches, which further improves the reconstruction efficiency and accuracy of some missing point cloud models. In this paper, 11 point cloud models are selected to carry out reconstruction experiments with some missing data information and structural features, and the experiments show that the proposed method has higher reconstruction efficiency than the L-Gan and PCN methods when the same dataset is used for training and testing. In the eleven test categories, the average improvement of the refactoring method in this paper is 12%-27%. The proposed method has a significant effect in dealing with the partial deletion reconstruction of small-scale models, and at the same time improves the efficiency and accuracy of reconstruction, which has good application value.