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Research on multi-source data fusion and high-precision mapping method for complex landforms based on computer

Alan Yang1

  • 1School of Architectural Engineering, Sanmenxia Polytechnic, Sanmenxia, 472000, Henan, China. alanyanglv0326@163.com.

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Summary

This study introduces SAAF-Net, an adaptive fusion framework for multi-source data in complex landforms. It significantly improves 3D model accuracy and completeness for digital twins by integrating semantic information.

Keywords:
3D reconstructionComputer visionDeep learningDigital twinHigh-precision mappingMulti-source data fusionPoint cloud registrationSemantic segmentation

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Area of Science:

  • Geographic Information Science
  • Computer Vision
  • Digital Twins
  • 3D Reconstruction

Background:

  • Digital twins and the metaverse necessitate high-fidelity 3D models of the physical world.
  • Single-sensor data acquisition in complex terrains (urban canyons, forests) suffers from blind spots and accuracy limitations.
  • Traditional data fusion methods struggle with semantic heterogeneity, leading to geometric distortion and detail loss.

Purpose of the Study:

  • To propose an adaptive fusion framework (SAAF-Net) for multi-source data in complex landforms.
  • To achieve high-precision semantic 3D models by deeply coupling semantic information.
  • To enhance the accuracy, completeness, and visual realism of 3D reconstructions for digital twin applications.

Main Methods:

  • Developed a two-stream deep semantic segmentation network (SegFormer, PointNeXt) for fine-grained feature classification (mIoU > 90%).
  • Introduced a semantic-guided cross-source registration (SW-ICP) algorithm to address heterogeneous data registration challenges.
  • Implemented a neural adaptive fusion model for dynamic confidence evaluation and optimal fusion of elevation and texture data.

Main Results:

  • SAAF-Net reduced root mean square error (RMSE) by 35%-48% compared to mainstream methods in urban and forest areas.
  • Achieved over 99% completeness in 3D reconstructions.
  • Significantly improved reconstruction quality in challenging areas like building edges, dense vegetation, and areas with complex lighting.

Conclusions:

  • The proposed SAAF-Net framework effectively integrates multi-source data for complex landforms.
  • Deeply coupled semantic information enhances the accuracy and realism of 3D models.
  • Provides crucial theoretical and technical support for building high-precision 3D bases for digital twin cities.