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PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching.

Lang Wu1, Kai Zhong1, Zhongwei Li1

  • 1State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

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|June 2, 2021
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Summary
This summary is machine-generated.

A new 3D local surface descriptor, the point-pair transformation feature histogram (PPTFH), enhances distinctiveness and robustness. This method improves 3D computer vision tasks like surface matching in challenging real-world conditions.

Keywords:
3D registration3D surface matchinglocal surface descriptorobject recognition

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Area of Science:

  • Computer Vision
  • 3D Geometry Processing

Background:

  • 3D local surface description is crucial for computer vision.
  • Current methods lack distinctiveness and robustness against noise, occlusion, and clutter.

Purpose of the Study:

  • To introduce a novel 3D local surface descriptor, the point-pair transformation feature histogram (PPTFH).
  • To enhance distinctiveness and robustness in 3D feature description for real-world scenes.

Main Methods:

  • A novel descriptor, PPTFH, is proposed using point-pair transformation features.
  • Local surface point-pair sets are partitioned into four subsets.
  • Darboux frame is utilized to compute point-pair transformation features and generate histograms.

Main Results:

  • PPTFH descriptor achieves superior descriptiveness and robustness compared to state-of-the-art algorithms.
  • Evaluated on popular benchmark datasets, demonstrating significant performance gains.
  • Proven benefits for 3D surface matching tasks across multiple datasets.

Conclusions:

  • The PPTFH descriptor offers a robust and distinctive solution for 3D local surface description.
  • Addresses limitations of existing methods in handling noisy and occluded 3D data.
  • Significantly advances the field of 3D computer vision and surface matching.