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Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Multiresolution mean shift clustering algorithm for shape interpolation.

Hung-Kuo Chu1, Tong-Yee Lee

  • 1Department of Computer Science and Infromation Engineering, National Cheng-Kung University, Tainan, Taiwan. hkchu@cise.nkcu.edu.tw

IEEE Transactions on Visualization and Computer Graphics
|July 11, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multiresolution mean shift (MMS) clustering algorithm for 3D shape interpolation. The method effectively handles significant pose variations in articulated models, generating aesthetically pleasing results with improved efficiency.

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Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Computational Geometry

Background:

  • 3D shape interpolation is challenging, especially for articulated models with varying poses.
  • Existing methods struggle to maintain shape integrity and rigidity during interpolation with significant pose changes.

Purpose of the Study:

  • To develop a robust method for 3D shape interpolation that accurately handles significant pose variations in articulated models.
  • To generate aesthetically pleasing and structurally consistent interpolated shapes.

Main Methods:

  • Proposed a novel multiresolution mean shift (MMS) clustering algorithm to extract near-rigid components from input shapes.
  • Constructed a common articulated structure by establishing hierarchical relationships between extracted components.
  • Implemented shape interpolation by combining global pose interpolation of components and local gradient field interpolation, followed by Poisson equation reconstruction.

Main Results:

  • Successfully generated high-quality 3D shape interpolations for articulated models with significant pose variations.
  • The proposed MMS algorithm effectively extracts near-rigid components and computes a common articulated structure.
  • Achieved comparable or superior results to state-of-the-art methods with improved computational efficiency.

Conclusions:

  • The novel MMS-based approach provides an effective solution for 3D shape interpolation with significant pose variations.
  • The method preserves shape integrity and generates visually appealing interpolations.
  • Demonstrated superior performance and efficiency compared to existing state-of-the-art techniques.