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<p>Point cloud data, particularly aerial point clouds, presents challenges due to its unstructured nature and low density. This paper introduces DFNet, a dual-focusing network designed to enhance aerial point cloud semantic segmentation. DFNet incorporates a shape-focus neighborhood refinement module to adaptively determine optimal neighborhood shapes and a local context focus module to prioritize discriminative local context features. Experimental results on the STPLS3D and ISPRS Vaihingen 3D datasets demonstrate state-of-the-art performance, where DFNet achieves outstanding mean intersection over union scores of 54.38% and 56.65%, respectively, to surpass existing methods. Our approach offers a robust solution for complex urban environment segmentation. The source code will be available at <a href="https://github.com/xianyu-wang/DFN.">https://github.com/xianyu-wang/DFN.</a></p>

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Dual-focusing network: enhancing aerial point cloud semantic segmentation with adaptive neighborhood refinement

  • 期刊论文

  • 发表日期：

    2026年4月5日

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  • Ke Zhang,
  • Xianyu Wang,
  • Yulin Wu,
  • Jingyu Wang

摘要

Point cloud data, particularly aerial point clouds, presents challenges due to its unstructured nature and low density. This paper introduces DFNet, a dual-focusing network designed to enhance aerial point cloud semantic segmentation. DFNet incorporates a shape-focus neighborhood refinement module to adaptively determine optimal neighborhood shapes and a local context focus module to prioritize discriminative local context features. Experimental results on the STPLS3D and ISPRS Vaihingen 3D datasets demonstrate state-of-the-art performance, where DFNet achieves outstanding mean intersection over union scores of 54.38% and 56.65%, respectively, to surpass existing methods. Our approach offers a robust solution for complex urban environment segmentation. The source code will be available at https://github.com/xianyu-wang/DFN.