【作者】 葛嵩；

【导师】 苏志勋；

【作者基本信息】 大连理工大学 ， 计算数学， 2009， 硕士

【摘要】 近年来点采样几何作为一种新的曲面表示方式,受到了广泛的关注。它无需存储和维护全局一致的拓扑信息,能对复杂的三维模型进行高效的绘制和灵活的几何处理,因此在处理复杂的或者动态改变形状的模型时,基于点的技术较之基于网格的技术有更高的灵活性。法向是点云的一个非常重要的几何信息,对点云的法向进行准确的估计,是在点云上进行其它操作的一个重要的基础步骤,因此对点云法向估计的研究具有重要的实际意义。给定一个从未知曲面上采样得到的点云,问题是如何准确估计点云中每一个点的法向。一些目前存在的算法,如基于拟合平面的法向估计算法,基于主成分分析的法向估计算法,基于标准奇异值分解的法向估计算法,基于Voronoi的法向估计算法等都可以对点云法向进行估计。但是通过采样得到的点云往往都伴随着大量的噪声,从而影响法向估计的准确性,这就要求点云法向估计的算法要具有较强的鲁棒性。然而上述这些算法的鲁棒性不强,因而导致法向估计的效果不理想。一种基于统计学习的Ensembles点云法向估计算法,在克服噪声和外部干扰上取得了很好的效果,但是由于其采样的随机性并且采用了相同的采样率,从而容易造成采样不均匀和局部信息丢失,导致估计结果不准确。本文提出了一种改进的Ensembles算法,通过引进分块采样策略及采用自适应的采样率,基本克服了原Ensembles算法的不足。同时,给出了一种新的带有权的平均公式,提高了算法的鲁棒性。更多还原

【Abstract】 As an alternative surface representation,point-based geometry has been drawn increased attention in recent years.Since this method does not have to store or maintain globally consistent topological information,and can provide efficient rendering and flexible geometry processing of highly complex 3D-models,it is more flexible than triangle meshes while handling highly complex or dynamically changing shapes.For point cloud data,the normal,a geometry information,is so important that the accurate estimation of it is a basic step for the operations on point clouds.Given a point cloud that presumably sampled from an unknown surface,the important is how to estimate the normal of each point.Some subsistent algorithms,the fitting surface based algorithm;principal component based algorithm;the standard singular value decomposition based algorithm;the Voronoi based algorithm,for example,give the methods that estimate the normal of point clouds.But a point cloud sampled is usually together with noise which affects the accuracy of estimation of normal.So this kind of algorithm requires strong robust.However,the robust of algorithms mentioned above are not strong so that the estimation of normal is not good.An ensemble normal estimation algorithm based on statistical learning gets a good effect on the data with noise and outliers.But due to the randomicity and the same rate of sample,it is easy to cause non-uniform sample and lose local information,which makes the estimation incorrect.The paper introduces an improved ensembles algorithm.By adding both a sub-block sample strategy and an adaptive sample rate,it covers the shortage of original algorithm.At the same time,the improved algorithm shows a new average formula with weight which enhances the robust of it.更多还原