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Edge-Point Cloud Fusion for Geometric Fitting of Cylinder Parameters Using Single-View RGB-D Data.

Huayan Zhang1, Jiaxin Liu1, Zhongkui Wang1

  • 1Department of Robotics, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Shiga, Japan.

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|March 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel edge-point cloud fusion method for accurately fitting cylinder parameters from noisy RGB-D camera data. The approach enhances geometric fitting by combining 2D image edges with 3D point clouds, improving robustness.

Keywords:
RGB-D datacylinder parametersedge–point cloud fusiongeometric fittingparameter optimization

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Area of Science:

  • Computer Vision
  • Geometric Modeling
  • Robotics

Background:

  • Accurate geometric fitting of cylinder parameters is crucial for industrial and perception tasks.
  • Consumer-level RGB-D cameras provide 3D point cloud data but are susceptible to noise, degrading fitting performance.
  • Existing methods struggle with noise in point cloud data, especially from curved surfaces.

Purpose of the Study:

  • To propose an edge-point cloud fusion approach for robust cylinder parameter fitting.
  • To leverage 2D image-domain edge constraints to mitigate noise in 3D point cloud data.
  • To improve the accuracy and robustness of geometric fitting from single-view RGB-D data.

Main Methods:

  • Developed a unified formulation to jointly optimize cylinder parameters using both 2D edge and 3D point cloud data.
  • Implemented an edge-point cloud fusion technique to integrate complementary information.
  • Utilized single-view RGB-D data for geometric fitting.

Main Results:

  • The proposed method demonstrated significant improvements in accuracy and robustness compared to traditional point cloud fitting methods.
  • Experimental results on real-world RGB-D data validated the effectiveness of the edge-point cloud fusion approach.
  • Explicitly incorporating edge information effectively mitigated the impact of noise in point cloud data.

Conclusions:

  • The edge-point cloud fusion approach offers a robust solution for cylinder parameter fitting from noisy RGB-D data.
  • This method enhances the reliability of geometric fitting in real-world perception and industrial applications.
  • The fusion of 2D and 3D data provides a powerful strategy for overcoming sensor noise limitations.