MS-SADNet: A Multi-Scale Self-Attention Decoder Network for Point Cloud Semantic Segmentation

Semantic segmentation of outdoor point clouds plays a vital role in applications such as autonomous driving and urban scene understanding. However, the uneven density of point clouds manifests as sparsity in distant regions and incomplete geometric information of objects, while the proportions of different object classes remain significantly imbalanced. These challenges generally result in poor segmentation performance for distant and fine-grained objects. In addition, many existing methods are not well-suited for large-scale point clouds due to costly sampling strategies and computationally intensive pre- and post-processing steps. To address these issues, we propose MS-SADNet, an efficient and lightweight neural network architecture. To reduce the computational burden of large-scale point cloud processing, the network adopts random sampling to lower computational overhead, thereby avoiding the efficiency bottlenecks introduced by complex sampling strategies. A multi-scale local feature aggregation module is further incorporated to enlarge the receptive field of local features, compensating for information loss caused by uneven density and enhancing the representation of fine-grained objects. Moreover, we design an attention-based decoder that captures long-range dependencies during feature upsampling, enabling dynamic fusion of local details and global semantic information, and strengthening the features of rare classes and geometrically incomplete objects through inter-object relationships. In addition, learnable parameters are introduced into the skip connections to overcome the gradient propagation limitations of traditional skip connections for long-tail classes, thereby improving multi-level feature fusion. Experiments on the Toronto3D, SemanticKITTI, and Semantic3D datasets demonstrate that the proposed method achieves promising segmentation performance, with notable improvements in the accuracy of distant sparse regions and fine-grained objects.