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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Updated: Aug 22, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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G2O-Pose: Real-Time Monocular 3D Human Pose Estimation Based on General Graph Optimization.

Haixun Sun1, Yanyan Zhang1, Yijie Zheng1

  • 1College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces G2O-pose, a fast method for monocular 3D human pose estimation. It achieves high running speeds for real-time applications by optimizing human pose as a graph.

Keywords:
general graph optimizationmonocular 3D human posemultiple constraintsreal-time

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Robotics

Background:

  • Monocular 3D human pose estimation lacks depth information, posing challenges.
  • Traditional methods are often too slow for real-time applications.

Purpose of the Study:

  • To propose a real-time method for monocular 3D human pose estimation.
  • To improve running speed without significantly compromising accuracy.

Main Methods:

  • Representing 3D human pose as a graph.
  • Utilizing general graph optimization (G2O) with multiple constraints.
  • Implementing constraints via 3D bone proportion recovery, human orientation classification, and reverse joint correction/suppression.

Main Results:

  • The G2O-pose method achieves high running speeds.
  • Outperforms previous non-deep learning methods in speed when human body depth is stable.
  • Demonstrates only a slight decrease in accuracy compared to traditional methods.

Conclusions:

  • G2O-pose offers a viable real-time solution for monocular 3D human pose estimation.
  • The graph optimization approach effectively addresses challenges posed by limited depth information.