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A Fast Mesh Simplification Algorithm Based on Octree with Quadratic Approximation

机译:基于二次近似的基于OctREE的快速网格简化算法

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Simplification is a hot topic in the field of mesh modeling. Most of the algorithms are progressive, and generating the intermediate models makes the algorithms less efficient. Vertex clustering is generally adopted to reduce the sample points, but the error control is hard to achieve. This paper presents a novel fast simplification algorithm which is based on octree with quadratic approximation. Our algorithm is not progressive and avoids creating any intermediate models. Hence there is a considerable improvement in efficiency. At the mean time, the error could be controlled as the subdivision process of the octree is based on a quadratic approximation of the local surface. The subdivision would stop only when the surface could be represented by a quadratic function, that is, the least-square error is less than a given threshold. Sample points are then clustered and represented by the most important ones, which give the contour of the model. Testing results show that the time and memory consumptions are significantly reduced.
机译:简化是网格建模领域的热门话题。大多数算法是渐进的,并且生成中间模型使算法效率较低。通常采用顶点群集来减少采样点,但难以实现错误控制。本文提出了一种新颖的快速简化算法,基于具有二次近似的Octree。我们的算法不是渐进的,避免创建任何中间模型。因此,效率显着提高。在平均时间时,可以控制误差,因为八角体的细分过程基于局部表面的二次近似。仅当表面可以通过二次函数表示时,仅停止该细分,即,最小二乘误差小于给定阈值。然后将样本点聚集并由最重要的聚集并表示,这给出了模型的轮廓。测试结果表明,时间和内存消耗显着降低。

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