A point cloud segmentation network with hybrid convolution and differential channels

In recent years, point-based segmentation methods have made significant progress in improving segmentation accuracy. However, existing approaches still suffer from several key limitations. Traditional convolution operations make it difficult to effectively model the irregular geometry in point cloud data, resulting in insufficient sensitivity to spatial details. Additionally, current methods have limitations in collaboratively modeling and integrating global and local information. For this reason, we propose a 3D segmentation network based on hybrid convolution and differential channels. Specifically, we design a hybrid convolutional feature extraction (HCFE) module for processing 3D semantic information and spatial information independently, using different convolution kernels to obtain the subtle geometric structure differences between points. Then, we propose a Differential Channel Feature Interaction (DCFI) Module to enhance the local details and global channel information through Differential Convolution (DCU) and a Simplified Channel Attention Mechanism (S_ECA), respectively, and adaptively fuse the two types of information by Dynamic Interaction Mechanism (DIM), achieving their cooperative optimization. Compared to existing methods, HDC_Net has clear advantages in detail capturing and the integration of local and global information. A large number of experiments demonstrate the effectiveness and superiority of the model proposed in this study.