Strengthening 3D Point Cloud Classification through Self-Attention and Plane Features

To address the unique attributes of three-dimensional point cloud data, this paper introduces an innovative architecture for precise 3D point cloud classification. Expanding on the PointMLP framework, we incorporate an embedding module that elevates the point cloud to higher-dimensional feature representations which are followed by geometric feature mapping and extraction modules to capture point cloud characteristics. To estimate local geometric structures, we use plane features to determine planes associated with nearby points. Additionally, we integrate self-attention mechanisms to capture intricate local geometric features. Moreover, MLP modules with residual connections are employed for efficient feature extraction. The derived features are then reduced in size using Max Pooling layers. For classification purposes, we utilize fully connected layers, batch normalization, activation functions, and random weight dropping techniques to enhance generalization and ensure robustness on unseen data. By adopting these architectural decisions, our proposed model achieves significant progress in accurately classifying 3D point clouds.