Measurable Semantic 3D Gaussian Splatting with Joint Region and Depth Supervision for Interactive Building Inspection

Key points

• This study proposes a measurable semantic 3D Gaussian Splatting (3DGS) framework with region and depth supervision for interactive building inspection from multi-view imagery.1
• Point-wise depth supervision guides Gaussian centers toward physical surfaces using reference geometry derived from multi-view stereo (MVS) point clouds, providing geometric anchors for measuring physical dimensions and areas.1
• On the synthetic UAV-inspection QuakeCity and smartphone-captured HKUPB datasets, the framework reduces Gaussian-center depth MAE by 58.1% and 86.8%, while its semantic field achieves mIoU values of 0.8529 and 0.8642.1
• These results show that the proposed framework extends 3DGS beyond visual reconstruction into a measurable, semantically structured, and interactive engineering representation for building inspection.1

Sources (1)

• [1]Measurable Semantic 3D Gaussian Splatting with Joint Region and Depth Supervision for Interactive Building Inspection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 07:33 AM

  Image-based 3D reconstruction provides a scalable representation for building inspection and maintenance.

  This study proposes a measurable semantic 3D Gaussian Splatting (3DGS) framework with region and depth supervision for interactive building inspection from multi-view imagery.

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