Enhanced Local Feature Learning With Simple Offset Attention for Semantic Segmentation of Large-Scale Point Clouds

The semantic segmentation network performance of large-scale outdoor point clouds is usually limited by the number of input point clouds. In the application of most methods, the point cloud is cut into small pieces as the training input, which will not only lead to heavy preprocessing burdens but also destroy the overall geometric structure of the scene. The transformer network has demonstrated remarkable advantages of attention mechanisms in focusing on crucial features and improving model performance. However, its training and inference on large-scale data are confined by computational complexity. To eliminate these challenges, the attention mechanisms are improved to enhance their performance in processing large-scale input data, while removing limitations imposed by computational complexity. Moreover, a novel local feature enhancement (LFE) module is developed to construct the LFE-Net, which can accurately and efficiently extract spatial and attribute features from local point clouds. In particular, an improved attention module, which is called simple offset attention (SOA), is adopted for local point cloud spatial feature learning. Compared with self-attention, SOA requires less memory and can better capture the fine-grained local features. Furthermore, to effectively avoid the destruction of object geometry and diminish the impact of sample imbalance, a training sample collection method based on the number of different classes is designed. To validate the effectiveness of this method, some experiments are conducted based on publicly accessible outdoor point cloud datasets. The results demonstrate that the LFE-Net can achieve substantial improvements compared with other cutting-edge network models.