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<p>Point Transformer can effectively capture both local information and long-range global context in point cloud data. However, its reliance on dividing local areas into separate tokens can result in a loss of instance structure, limiting its feature representation ability in large-scale urban aerial point clouds, especially the fine-grained classification of ground objects. To address these limitations, we propose a novel Point Transformer-based Multi-feature Fusion (PTMF) Network that explicitly integrates geometric features into the Point Transformer architecture to classify large-scale aerial point clouds. The fusion of geometric features effectively complements global contextual feature extraction that is solely based on positional relationships with prior information. Specifically, the PTMF network fuses down-sampled geometric and inherent point cloud features at each stage, using the Transition Up module to up-sample mapping features. Our experiments on the SensatUrban and DALES datasets demonstrate significant improvements, achieving mIoU scores of 63.52% and 82.18%, respectively, outperforming existing methods.</p>

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An aerial point cloud classification using point transformer via multi-feature fusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Jiechen Pan,
  • Jiayin Cao,
  • Shuai Xing,
  • Mofan Dai,
  • Jikun Liu,
  • Xuying Wang,
  • Yunsheng Zhang,
  • Gaoshuang Huang

摘要

Point Transformer can effectively capture both local information and long-range global context in point cloud data. However, its reliance on dividing local areas into separate tokens can result in a loss of instance structure, limiting its feature representation ability in large-scale urban aerial point clouds, especially the fine-grained classification of ground objects. To address these limitations, we propose a novel Point Transformer-based Multi-feature Fusion (PTMF) Network that explicitly integrates geometric features into the Point Transformer architecture to classify large-scale aerial point clouds. The fusion of geometric features effectively complements global contextual feature extraction that is solely based on positional relationships with prior information. Specifically, the PTMF network fuses down-sampled geometric and inherent point cloud features at each stage, using the Transition Up module to up-sample mapping features. Our experiments on the SensatUrban and DALES datasets demonstrate significant improvements, achieving mIoU scores of 63.52% and 82.18%, respectively, outperforming existing methods.