DGONN: Depthwise Dynamic Graph Overparameterized Neural Network for 3D Point Cloud Object Recognition

Rapid advancement in 3D point cloud object recognition is crucial for robotics, autonomous driving, and augmented reality applications. The traditional methods, including PointNet and its successors, though effective in handling unordered point cloud data, need help capturing local structures accurately and efficiently. This paper introduces a novel architecture, the Depthwise Dynamic Graph Overparameterized Neural Network (DGONN), which enhances point cloud object recognition by integrating graph-based features with overparameterized networks. Our method leverages local geometric formations through a neighborhood graph. It performs operations similar to convolutions, utilizing edge convolution (EdgeConv) and depthwise overparameterized convolution (DO-Conv) for dynamic graph updates and efficient feature representation. The proposed DGONN architecture dynamically updates the graph …