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Related Experiment Video

Updated: Jun 27, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

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Published on: April 21, 2023

Weakly Textured Objects Pose Estimation: A Comprehensive Review.

Jialun Li1, Fanwu Meng1, Shiyang Mao1

  • 1School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This review explores pose estimation for objects lacking texture, crucial for robot grasping. Future research should focus on multimodal fusion, lightweight models, and efficient annotation for improved performance.

Keywords:
computer visiondeep learningpose estimationweak texture

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Last Updated: Jun 27, 2026

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Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Pose estimation is vital for machine vision applications like robot grasping and augmented reality.
  • Weakly textured objects present significant challenges due to limited features, hindering robot grasping.
  • Existing methods struggle with generalization, real-time performance, and data acquisition.

Purpose of the Study:

  • To systematically review recent advancements in weakly textured object pose estimation.
  • To categorize existing pose estimation methods, including traditional and deep learning approaches.
  • To identify key challenges and future research directions in this domain.

Main Methods:

  • Classifying pose estimation methods into traditional and deep learning categories.
  • Further dividing deep learning methods into instance-level, category-level, and unseen object-level.
  • Analyzing core issues such as generalization limitations, real-time performance, and data bottlenecks.

Main Results:

  • Identified pose estimation for weakly textured objects as a critical bottleneck in robot grasping.
  • Summarized current research progress and categorized various pose estimation techniques.
  • Highlighted limitations in generalization, real-time processing, and data availability.

Conclusions:

  • Future research should prioritize multimodal fusion, lightweight model design, and low-cost annotation technologies.
  • Addressing these core issues will enhance algorithm design and engineering applications.
  • This review provides a valuable reference for the field of weakly textured object pose estimation.