Simplification algorithm for large polygonal model in distributed environment

Abstract

Polygonal models have grown rapidly in complexity over recent years, yet most conventional simplification algorithms were designed to handle modest size datasets of a few tens of thousands of triangles. We present a parallel simplification method for large polygonal models. Our algorithm will partition the original model firstly, send each portion to a slave processor, simplify them concurrently, and merge them together lastly. We give an efficient method to deal with the problem of partition border and portion merging. With parallel implementation, the algorithm can handle extremely large data set, and speed up the execution time. Experiment shows that our algorithm can produce approximations of high quality. © Springer-Verlag Berlin Heidelberg 2007.