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Towards Semantic Photogrammetry: Generating Semantically Rich Point Clouds from Architectural Close-Range

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Summary

This study introduces deep learning for semantic image segmentation in 3D reconstruction. The method automatically generates classified point clouds, streamlining the photogrammetric workflow.

Keywords:
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Area of Science:

  • Computer Vision
  • Photogrammetry
  • Artificial Intelligence

Background:

  • Manual semantic enrichment of 3D point clouds is time-consuming.
  • Deep learning has advanced semantic segmentation in 2D and 3D.
  • Integrating semantic classification early in photogrammetry is challenging.

Purpose of the Study:

  • To automate the creation of semantically classified dense point clouds.
  • To integrate deep learning-based semantic image segmentation into photogrammetric workflows.
  • To enable semantic classification at the start of 3D reconstruction.

Main Methods:

  • Utilized a pre-trained neural network for automatic image masking based on predefined classes.
  • Employed image masks to constrain dense image matching within specific semantic classes.
  • Developed a workflow for automatic semantic point cloud classification from images.

Main Results:

  • Achieved automatic generation of semantically classified dense point clouds.
  • Demonstrated promising results for the automated photogrammetric workflow.
  • Obtained high Intersection over Union (IoU) scores for specific classes like building facades (0.79) and windows (0.77).

Conclusions:

  • The proposed deep learning approach successfully automates semantic point cloud classification.
  • The method offers a feasible and efficient alternative to manual point cloud enrichment.
  • This integration enhances the semantic richness of 3D reconstructions from the outset.