MuSPaCSA: Multi-Scale Parallel-Channel Self-Attention Network for Point Cloud Classification and Segmentation

Abstract

Point cloud classification and segmentation are fundamental tasks in 3D computer vision. Recently, deep learning-based methods, particularly 3D Transformers, have demonstrated their effectiveness across a variety of point cloud tasks. However, transformer-based methods embed position information into feature vectors, which can introduce a significant computational cost. Additionally, these approaches often struggle to adaptively extract different features across varying receptive fields, which limits their performance in various tasks. To address these challenges, we propose a novel Multi-Scale Parallel-Channel Self-Attention (MuSPaCSA) network, designed with a multi-scale feature extraction architecture by stacking Parallel-Channel Self-Attention (PaCSA) layers for classification and segmentation tasks. Specifically, our MuSPaCSA employs the PaCSA module to extract essential semantic and spatial features. The core components of the PaCSA module include the Semantic-Spatial Integration (SSI) and Adaptive Self-Attention (ASA) modules. The SSI module employs a parallel-channel approach to integrate semantic and spatial information, enabling the representation of high-dimensional structural features in point clouds. The ASA module calculates adaptive weights to aggregate rich, high-dimensional structural features from neighboring nodes in a lightweight manner. Through the multi-scale feature fusion architecture of MuSPaCSA, local and global features, as well as semantic and spatial features, are effectively integrated, significantly enhancing the model’s representational capacity. Extensive experiments demonstrate that our model achieves superior performance and results with lower computational cost compared to competing methods.