Issues for Data Reduction of Dense Three-Dimensional Data

摘要

Acquiring a large quantity of three-dimensional data has become commonplace 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.