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Reconstructing the Curve-Skeletons of 3D Shapes Using the Visual Hull.

Marco Livesu¹, Fabio Guggeri, Riccardo Scateni

¹Dipartimento di Matematica e Informatica, Universita` degli Studi di Cagliari, via Ospedale, 72, Cagliari 09124, Italy. marco.livesu@unica.it

IEEE Transactions on Visualization and Computer Graphics

|March 7, 2012

Summary

This study introduces a novel method for extracting curve-skeletons, essential shape descriptors, by mimicking human shape perception. The technique is robust across various 3D data types and conditions.

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Area of Science:

Computer Vision and Graphics
Computational Geometry
Shape Analysis

Background:

Curve-skeletons are crucial for shape representation and analysis in diverse fields.
Existing curve-skeleton extraction methods have limitations in terms of data type compatibility and robustness.
Human shape perception offers an intuitive model for shape descriptor extraction.

Purpose of the Study:

To develop a novel curve-skeleton extraction technique inspired by human shape perception.
To infer curve-skeletons and maximal inscribed ball radii for 3D shapes.
To demonstrate the method's applicability to various 3D data representations.

Main Methods:

Utilizes epipolar geometry and visual hull concepts.
Analyzes medial axes of projected shape views using stereographic vision.
Infers curve-skeletons from projected shape information.

Main Results:

Successfully infers curve-skeletons and radii for a broad class of 3D shapes.
The method is invariant to noise, pose, and resolution.
Demonstrates robustness with incomplete datasets and various data types (meshes, voxel models, point clouds).

Conclusions:

The proposed method offers a robust and versatile approach to curve-skeleton extraction.
It effectively captures essential shape features by simulating human-like shape deduction.
The technique holds significant potential for applications in shape matching, retrieval, medical imaging, and animation.