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3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview.

Byung-Seo Park1, Woosuk Kim1, Jin-Kyum Kim1

  • 1Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.

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Summary
This summary is machine-generated.

This study introduces a novel method for multi-view RGB-D camera calibration using human skeleton joints as feature points, enabling efficient 3D point cloud registration without special tools.

Keywords:
3D registrationRGB-Djoint setpoint cloudpose estimation

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Area of Science:

  • Computer Vision
  • Robotics
  • 3D Reconstruction

Background:

  • Multi-view camera calibration is crucial for accurate 3D data acquisition.
  • Traditional calibration methods often rely on specialized tools like chessboards.
  • Existing techniques may struggle with incomplete or noisy data.

Purpose of the Study:

  • To develop an efficient and tool-free method for multi-view RGB-D camera calibration.
  • To leverage human pose estimation for generating calibration feature points.
  • To achieve accurate 3D static-point cloud registration.

Main Methods:

  • Utilizing 3D joint coordinates derived from human pose estimation as feature points.
  • Integrating multiple incomplete joint sets into a unified set for calibration.
  • Employing temporal iteration for optimizing calibration accuracy via extrinsic matrix estimation.

Main Results:

  • Demonstrated successful calibration of multi-view RGB-D cameras using incomplete human joint sets.
  • Achieved efficient calibration without the need for specialized calibration tools.
  • Validated the proposed method's effectiveness through experimental results.

Conclusions:

  • Human skeleton joints are viable and efficient feature points for multi-view camera calibration.
  • The proposed RGB-D-based calibration algorithm effectively handles incomplete joint data.
  • This technique offers a practical alternative for 3D point cloud registration applications.