Powerful Sample Reduction Techniques for Constructing Effective Point Cloud Object Classification Models

Due to the large volume of raw data in 3D point clouds, downsampling techniques are crucial for reducing computational load and memory usage to improve the training of 3D point cloud models. This paper plans to conduct research using the ModelNet40 dataset. Our proposed method is based on the PointNext architecture, an improved version of PointNet++ that significantly enhances performance through optimized training strategies and adjusted receptive fields. During the model training process, we employ the farthest point sampling method for downsampling. Specifically, we use an improved attention-based point cloud edge sampling (APES) method for downsampling, where we compute the density of each point and set the size of the neighbor K value to effectively retain feature points during downsampling. Our improved method captures edge points more effectively than the original APES method. By adjusting the architecture, our method, combined with the farthest point sampling method, not only reduced the average training time by nearly 15% compared to PointNext-s, but also improved accuracy from 93.11% to 93.57%.