Fast and Reliable Decimation of Polygonal Models Based on Volume and Normal Field
Fast, reliable, and feature preserving automatic decimation of polygonal models is a challenging task. Exploiting both local volume and normal field variations in a novel way, a two phase decimation algorithm is proposed. In the first phase, a vertex is selected randomly using the measure of geometric fidelity that is based on normal field variation across its one-ring neighborhood. The selected vertex is eliminated in the second phase by collapsing an outgoing half-edge that is chosen by using volume based measure of geometric deviation. The proposed algorithm not only has better speed-quality trade-off but also keeps visually important features even after drastic simplification in a better way than similar state-of-the-art best algorithms; subjective and objective comparisons validate the assertion. This method can simplify huge models efficiently and is useful for applications where computing coordinates and/or attributes other than those attached to the original vertices is not allowed by the application and the focus is on both speed and quality of LODs.
KeywordsGeometric Deviation Polygonal Model Memory Overhead Vertex Attribute Huge Model
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