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MFPNet: A Semantic Segmentation Network for Regular Tunnel Point Clouds Based on Multi-Scale Feature Perception.

Junwei Tong1, Min Ji1,2,3, Pengfei Song1

  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.

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|February 13, 2026
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Summary
This summary is machine-generated.

This study introduces MFPNet, a novel network for tunnel point cloud semantic segmentation. It enhances perception accuracy by effectively fusing multi-scale features, improving 3D understanding for intelligent tunnel management.

Keywords:
feature fusionmulti-scale perceptionsemantic segmentationtunnel point clouds

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Area of Science:

  • Computer Vision
  • Geospatial Analysis
  • Machine Learning

Background:

  • Tunnel point cloud semantic segmentation is crucial for infrastructure management.
  • Challenges include indistinct boundaries and fine-grained category discrimination.

Purpose of the Study:

  • To propose MFPNet, a multi-scale feature perception network for tunnel point cloud semantic segmentation.
  • To address limitations of existing methods in complex tunnel environments.

Main Methods:

  • Kernel convolution for modeling local point cloud geometries.
  • Error-feedback-based local-global feature fusion mechanism.
  • Adaptive feature re-calibration and cross-scale contextual correlation.

Main Results:

  • MFPNet achieved 87.5% mIoU, outperforming PointNet++ and RandLA-Net by 5.1% to 33.0%.
  • Overall classification accuracy reached 96.3%.
  • Demonstrated significant improvements in segmentation accuracy and category balance.

Conclusions:

  • MFPNet provides high-precision 3D semantic understanding for complex tunnel environments.
  • Offers robust technical support for tunnel digital twins and intelligent detection.