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Three-Dimensional Mesh Simplification using Normal Variation Error Metric and Modified Subdivided Edge Classification

机译:使用正态变化误差度量和修正的细分边缘分类的三维网格简化

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In order to transmit or store three-dimensional (3-D) mesh models efficiently, we need to simplify them. Although the quadric error metric (QEM) provides fast and accurate geometric simplification of 3-D mesh models, it cannot capture discontinuities faithfully. Recently, an enhanced QEM based on subdivided edge classification has been proposed to handle this problem. Although it can capture discontinuities well, it has slight degradation in the reconstruction quality. In this paper, we propose a novel mesh simplification algorithm where we employ a normal variation error metric, instead of QEM, to resolve the quality degradation issue. We also modify the subdivided edge classification algorithm to be cooperative with the normal variation error metric while preserving discontinuities. We have tested the proposed algorithm with various 3-D VRML models. Simulation results demonstrate that the proposed algorithm provides good approximations while maintaining discontinuities well.
机译:为了有效地传输或存储三维(3-D)网格模型,我们需要简化它们。尽管二次误差度量(QEM)提供了3D网格模型的快速,准确的几何简化,但是它不能如实地捕获不连续性。最近,已经提出了一种基于细分边缘分类的增强型QEM来解决此问题。尽管可以很好地捕获不连续性,但重建质量会略有下降。在本文中,我们提出了一种新颖的网格简化算法,其中我们使用正态变化误差度量代替QEM来解决质量下降问题。我们还修改了细分的边缘分类算法,使其与正常变化误差度量保持协作,同时保留了不连续性。我们已经使用各种3-D VRML模型测试了提出的算法。仿真结果表明,该算法在保持不连续性的同时提供了良好的近似效果。

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