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The Technology Research Based On Least Square Method For Extracting Quadric Surface

机译:基于基于最小二乘法的技术研究

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In this paper, the discrete point cloud data is directly used to extract quadric surface, with the single type of the point cloud data, the surface is firstly recognized. And then, according to different types of quadric surface, using the geometric parameter equation, the technology of extracting quadric surface can be achieved based on least square method. The results of the study show that: For normal data or less noisy data, the accuracy of calculation of linear least square method is the highest. For the nonlinear square method, on the other hand, the calculation precision is the lowest. However, the computational efficiency of the nonlinear least square method is higher than the linear least square method. The least square laws are more sensitive to noise data. With the increasing of the increased noise data, the extraction accuracy of the results will be affected by certain influence, but its computation efficiency is higher. Research results to practical engineering application of least square method to extract the quadratic surface have certain guiding significance.
机译:在本文中,离散点云数据直接用于提取Quadric Surface,用单一类型的点云数据,首先识别表面。然后,根据不同类型的二次表面,使用几何参数方程,可以基于最小二乘法实现提取Quadric表面的技术。研究结果表明:对于正常数据或较少的嘈杂数据,线性最小二乘法计算的准确性最高。另一方面,对于非线性方形方法,计算精度是最低的。然而,非线性最小二乘法的计算效率高于线性最小二乘法。最小二乘法对噪声数据更敏感。随着噪声数据的增加,结果的提取精度将受到某种影响的影响,但其计算效率较高。研究结果对最小二乘法提取二次表面的实际工程应用具有一定的指导意义。

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