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Slice-Guided Components Detection and Spatial Semantics Acquisition of Indoor Point Clouds.

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  • 1School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China.

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This study introduces a novel slice-guided algorithm for detecting complex indoor scene components and their spatial relationships. The method effectively extracts meaningful parts and their connections, improving indoor scene understanding.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • 3D Scene Understanding

Background:

  • Extracting indoor scene components and their spatial relationships is vital for scene reconstruction and understanding.
  • Detecting complex-shaped indoor components currently presents significant challenges in computer vision.

Purpose of the Study:

  • To develop a robust algorithm for detecting complex-shaped indoor scene components.
  • To accurately obtain the spatial relationships between these components for enhanced scene understanding.

Main Methods:

  • A slice-guided algorithm was proposed, involving slicing indoor scene models into layers.
  • Two-dimensional (2D) profiles from neighboring slices were clustered based on spatial proximity and similarity.
  • An ontology was constructed to model commonsense knowledge, enabling inference of spatial semantics and creation of a semantic graph of spatial relationships (SGSR).

Main Results:

  • The proposed method effectively detects complex-shaped indoor scene components.
  • The spatial relationships between indoor components are accurately acquired.
  • The approach demonstrates improved capabilities in indoor scene analysis.

Conclusions:

  • The slice-guided algorithm offers a powerful solution for indoor scene component detection and spatial relationship acquisition.
  • This method advances the field of 3D scene understanding by addressing the challenge of complex shapes.
  • The integration of ontology and semantic graphs enhances the representation of spatial relationships.