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Related Experiment Video

Updated: Jul 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Point fingerprint: a new 3-D object representation scheme.

Yiyong Sun¹, Joonki Paik, A Koschan

¹Dept. of Electr. & Comput. Eng., Univ. of Tennessee, Knoxville, TN, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

This summary is machine-generated.

This study introduces point fingerprints, a novel 2-D contour representation for efficient surface matching. This method accurately identifies corresponding points on surfaces, improving pose estimation for 3-D data.

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Area of Science:

Computer Vision
Geometric Modeling
3D Reconstruction

Background:

Surface matching is crucial for 3D data analysis and computer vision tasks.
Existing methods often face challenges with efficiency and accuracy in representing complex surfaces.

Purpose of the Study:

To propose an efficient and accurate surface representation method for surface matching.
To introduce a novel feature carrier, termed 'point fingerprint', for discriminating surface points.

Main Methods:

Generating a point fingerprint for each surface point using projected geodesic circles on the tangent plane.
Utilizing the point fingerprint's pattern for matching corresponding points across different views.
Incorporating curvature, color, and other information into the fingerprint to enhance matching.

Main Results:

Demonstrated faster matching processes compared to traditional 2-D image comparison.
Introduced a new candidate point selection method based on fingerprint irregularity.
Successfully applied the point fingerprint method to pose estimation using real-world range data.

Conclusions:

The point fingerprint offers an efficient and effective surface representation for matching.
This method significantly improves accuracy and speed in surface matching and pose estimation tasks.