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6DoF Pose Estimation of Transparent Objects: Dataset and Method.

Yunhe Wang1, Ting Wu1, Qin Zou1

  • 1School of Computer Science, Wuhan University, Wuhan 430072, China.

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Summary

This study introduces HFF6DoF, a novel network for six-degrees-of-freedom (6DoF) pose estimation of transparent objects. The method significantly improves accuracy on a new dataset, outperforming existing approaches.

Keywords:
pose estimationrobotic graspingsemantic segmentationtransparent object

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Six-degrees-of-freedom (6DoF) pose estimation is crucial for robotic grasping.
  • Transparent objects pose a significant challenge for existing 6DoF pose estimation methods due to their lack of texture.
  • Accurate pose estimation is vital for enabling robots to interact with and manipulate transparent objects effectively.

Purpose of the Study:

  • To propose a novel hierarchical feature fusion network (HFF6DoF) for accurate 6DoF pose estimation of transparent objects.
  • To address the limitations of current methods in handling textureless transparent objects.
  • To introduce a new benchmark dataset for evaluating 6DoF pose estimation of transparent objects.

Main Methods:

  • A dual-branch network extracts appearance and geometry features from RGB-D images.
  • Hierarchical fusion of features aggregates information for improved representation.
  • A decoding module performs semantic segmentation and keypoint vector-field prediction.
  • Random Sample Consensus (RANSAC) and Least-Squares Fitting are used for pose calculation based on predicted outputs.
  • A new dataset, TDoF20, comprising 61,886 RGB-D image pairs of 20 transparent object types was created.

Main Results:

  • The proposed HFF6DoF network demonstrates superior performance compared to state-of-the-art methods.
  • The method achieved a significant improvement on the TDoF20 dataset, with an average ADD of 50.5%.
  • The hierarchical feature fusion approach effectively leverages both appearance and geometric information.

Conclusions:

  • The HFF6DoF network offers a robust solution for the challenging problem of 6DoF pose estimation for transparent objects.
  • The developed TDoF20 dataset provides a valuable resource for future research in this area.
  • The findings advance the capabilities of robotic systems in handling transparent objects.