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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Xi Chen,
  • Liu Yang,
  • Han Bao,
  • Meiji Chen,
  • Hao Lin,
  • Yusen Gao,
  • Pingzhi Fan

摘要

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.