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Pose-invariant matching for non-rigid 3D models using Isomap.

Hairong Jin1, Haichao Huang2, Zhiqiang Wang2

  • 1Polytechnic Institute, Zhejiang University, Ningbo, Zhejiang, China.

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|March 16, 2022
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Summary
This summary is machine-generated.

This study introduces a new 3D model matching algorithm using Isomap to map vertices to 1D space. The method creates pose-invariant features for robust and accurate 3D model comparison.

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Area of Science:

  • Computer Vision
  • Geometric Modeling
  • Machine Learning

Background:

  • 3D mesh models are widely used, necessitating effective matching algorithms.
  • Existing methods may lack invariance to various transformations and poses.
  • Robustness to noise is crucial for real-world applications.

Purpose of the Study:

  • To propose a novel algorithm for matching 3D mesh models.
  • To achieve invariance to translation, rotation, scaling, and pose.
  • To ensure robustness against noise in 3D model data.

Main Methods:

  • Vertices of 3D mesh models are mapped to 1D space using Isomap.
  • A pose-invariant feature set is constructed from the 1D vertex representations.
  • Model similarity is computed by comparing these feature sets.

Main Results:

  • The proposed algorithm demonstrates invariance to translation, rotation, and scaling.
  • The method is also invariant to different poses of 3D models.
  • Experimental results confirm the algorithm's robustness to noise.

Conclusions:

  • The novel 3D model matching algorithm is effective and versatile.
  • The approach offers significant advantages in handling pose variations and noise.
  • This method enhances the reliability of 3D model comparison in various applications.