Attention-Based Deep Neural Network for Point Cloud Learning

Point cloud learning is a vital task in the field of computer vision. Due to the irregularity and disorderliness of point clouds, learning their features has been a challenging issue for a long time. Attention mechanisms have been widely applied in deep learning and have achieved good results in point cloud learning tasks. However, existing models mainly focus on local features, and the stacking of numerous attention layers requires significant computational resources. To address these problems, this study first introduces a framework that considers both local and global features in a parallel manner. Based on this framework, we propose two practical implementations, PCAttn and PCTrans. The PCAttn explores the effectiveness of the channel and spatial attention mechanism, while the PCTrans introduces the self-attention mechanism to the point cloud learning. These models are tested on two publicly available benchmark datasets. Experimental results demonstrate that the proposed methods exhibit high performance and computational efficiency. PCAttn achieves 93.2% overall accuracy on the ModelNet40 dataset and 84.6% overall accuracy on the ScanObjectNN dataset with only 0.61M parameters and 3.4G floating point operations. PCTrans achieves 93.1% overall accuracy on the ModelNet40 dataset and 83.3% overall accuracy on the ScanObjectNN dataset with only 0.68M parameters and 0.35G floating point operations.