An efficient local clustering approach for simplification of 3D point-based computer graphics models

Given a point-based 3D computer graphics model which is defined by a point set P(P={p_iisinR³}) and a desired reduced number of output samples N_s, the simplification approach finds a point set P_s which (i) satisfies |P_s |=N_s(|P_s| is the cardinality of P_s) and (ii) minimizes the difference of the corresponding surface S_s(defined by P_s) and the original surface S(defined by P). Although a number of previous approaches have been proposed for simplification, most of them (i) do not focus on point-based 3D models, (ii) do not consider efficiency, quality and generality together. In this paper, we introduce an adaptive simplification method (ASM) which is an efficient technique for simplifying point-based complex 3D model. ASM achieves low running time by clustering the points locally based on the preservation of geometric characteristics. Finally, we analyze the performance of ASM and show that it outperforms most of the current state-of-the-art methods in terms of efficiency, quality and generality