Rand transformer net: An efficient network for semantic segmentation of railway engineering entities based on 3D point cloud

3D point clouds are widely utilized in critical vision tasks such as autonomous driving, augmented reality, and model reconstruction. Given the unstructured nature and large-scale characteristics of point cloud data, 3D point semantic segmentation often faces challenges in achieving both high accuracy and computational efficiency. To better balance accuracy and performance for domain-specific applications, this paper proposes a lightweight Rand Transformer Net (RTN), which constructs a more efficient multi-scale feature extraction module by using a random downsampling strategy and incorporates a specially designed Rand Transformer Block to capture local geometric features of point clouds. To address the issue of semantic ambiguity in boundary regions, A novel loss function, termed ABL loss, is introduced to impose additional constraints derived from labeled boundaries. Experimental results on a newly introduced Bridge Dataset, which is composed of large-scale point cloud-based bridge components, demonstrate that RTN achieves superior performance over the majority of existing point cloud semantic segmentation methods, with strong scalability and efficiency advantages in handling large-scale point cloud scenes.