Date of Award
Open Access Dissertation
Peter G Binev
We address critical issues arising in the practical implementation of processing real point cloud data that exhibits irregularities. We develop an adaptive algorithm based on Learning Theory for processing point clouds from a stationary sensor that standard algorithms have difficulty approximating. Moreover, we build the theory of distribution-dependent subdivision schemes targeted at representing curves and surfaces with gaps in the data. The algorithms analyze aggregate quantities of the point cloud over subdomains and predict these quantities at the finer level from the ones at the coarser level.
Diefenthaler, K. H.(2013). Analysis and Processing of Irregularly Distributed Point Clouds. (Doctoral dissertation). Retrieved from http://scholarcommons.sc.edu/etd/2495