Point Cloud Semantic Segmentation Network Design with Neighborhood Feature Enhancement

Abstract

The complex structures and diverse object categories in indoor environments pose significant challenges for point cloud semantic segmentation. To address the insufficient capability of extracting local features in complex scenes, this paper proposes a point cloud segmentation network based on neighborhood feature enhancement termed PKA-Net. First, to obtain richer and more discriminative feature representations, we design a local feature encoding module that extracts geometric features, color information, and spatial information from local regions of the point cloud for joint feature encoding. Furthermore, we enhance the hierarchical feature extraction by integrating Kolmogorov–Arnold Networks (KAN) to form the SAPK module, improving the network’s ability to fit complex geometric structures. A residual structure is also adopted to optimize feature propagation and alleviate the problem of gradient vanishing. Finally, we propose the dual attention mechanism C-MSCA, which dynamically selects and strengthens key features through the synergistic action of channel and spatial attention, enhancing the network’s perception of local details and global structure. To evaluate the performance of the proposed PKA-Net, extensive experiments were conducted on the S3DIS dataset. Experimental results demonstrate that PKA-Net improves OA by 2.1%, mAcc by 2.9%, and mIoU by 4% compared to the baseline model. It outperforms other mainstream models, delivering enhanced overall segmentation performance.