Issues for data reduction of dense three-dimensional data

Joseph H. Nurre; Jennifer J. Whitestone; Dennis B. Burnsides; David M. Hoeferlin

doi:10.1117/12.222715

3 October 1995 Issues for data reduction of dense three-dimensional data

Joseph H. Nurre, Jennifer J. Whitestone, Dennis B. Burnsides, David M. Hoeferlin

Proceedings Volume 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling; (1995) https://doi.org/10.1117/12.222715
Event: Photonics East '95, 1995, Philadelphia, PA, United States

Abstract

Acquiring a large quantity of 3D data has become common plane with the advent of new technologies. Reducing the number of data points improves processing speed and storage requirements. Astute data reduction requires an understanding of the correlation between data measures and geometric measures. These relationships are dependent upon the data reduction algorithm used. This paper investigates these relationships for a small number of data reduction algorithms. A framework is presented for tracking these changes and for assisting a user in identifying the most appropriate data reduction method for their application.

Citation Download Citation

Joseph H. Nurre, Jennifer J. Whitestone, Dennis B. Burnsides, and David M. Hoeferlin "Issues for data reduction of dense three-dimensional data", Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); https://doi.org/10.1117/12.222715

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