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High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel.

Dongbo Yu1, Jun Xiao1, Ying Wang1

  • 1School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China.

Sensors (Basel, Switzerland)
|August 6, 2020
PubMed
Summary

This study introduces a novel high-precision plane detection method for 3D rock-mass point clouds. The approach effectively segments complex rock structures, improving numerical model construction in rock-mass engineering.

Keywords:
high-precisionpatch-basedplane detectionregion growingrock masssupervoxelvoxel

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Area of Science:

  • Geotechnical Engineering
  • Computer Vision
  • Geology

Background:

  • Accurate detection of planar structures in rock-mass point clouds is vital for constructing lightweight numerical models.
  • Existing methods struggle with the complex and unpredictable surface structures of rock masses, limiting thorough segmentation.
  • High-quality geological models depend on precise surface analysis, which is currently a challenge.

Purpose of the Study:

  • To propose a high-precision plane detection approach for 3D rock-mass point clouds.
  • To address the limitations of existing techniques in segmenting cluttered and unpredictable rock mass surfaces.
  • To achieve detailed and accurate plane detection for improved rock-mass engineering applications.

Main Methods:

  • Utilizes spatial grids for fast voxel segmentation of the input point cloud.
  • Employs a local coplanarity test and edge information calculation to extract major plane segments.
  • Incorporates supervoxel segmentation and a patch-based region growing strategy for detailed plane reconstruction.

Main Results:

  • The proposed method demonstrates high-precision plane detection capabilities for rock-mass point clouds.
  • Achieves a high recall rate in identifying planar structures within complex geological data.
  • Experimental validation using artificial and real-world rock-mass point clouds confirms superior performance.

Conclusions:

  • The developed approach offers a significant advancement in the accurate segmentation of rock-mass point clouds.
  • Enables the creation of more detailed and reliable numerical models for rock-mass engineering.
  • The method's effectiveness in handling complex surfaces and ensuring high recall rate is validated.