Improved PointNet Classification Network Using Clustering Algorithms

PointNet was a breakthrough in neural networks for directly processing point cloud data, ensuring transformation and input invariance with a simple, efficient structure. However, it neglects the relationships between individual points’ features. This paper introduces an improved model, KMeans-PointNet, which combines PointNet with K-Means clustering. By clustering point cloud data and extracting global features from each cluster, the model enhances local feature utilization. Experiments on the ModelNet40 dataset show that KMeans-PointNet improves overall recognition accuracy by 3% and classification accuracy by 2% compared to the original PointNet.