>Due to the huge volume and complex structure, simplification of point clouds is an important technique in pr'/> Feature preserving multiresolution subdivision and simplification of point clouds: A conformal geometric algebra approach
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首页> 外文期刊>Mathematical Methods in the Applied Sciences >Feature preserving multiresolution subdivision and simplification of point clouds: A conformal geometric algebra approach
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Feature preserving multiresolution subdivision and simplification of point clouds: A conformal geometric algebra approach

机译：保存多分辨率细分和点云简化的功能：一个共形几何代数方法

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>Due to the huge volume and complex structure, simplification of point clouds is an important technique in practical applications. However, the traditional algorithms often lose geometric information and have no dynamic expanding structure. In this paper, a new simplification algorithm is proposed based on conformal geometric algebra. First of all, a multiresolution subdivision is constructed by the sphere tree, which computes the minimal bounding spheres with the help of k‐means clustering, and then 2 kinds of simplification methods with full advantages of distance computing convenience are applied to carry out self‐adapting simplification. Finally, several comparisons with original data or other algorithms are implemented from visualization to parameter contrast. The results show that the proposed algorithm has good effect not only on the local details but also on the overall error rate.
机译： >由于卷和复杂的结构巨大，点云的简化是一个实际应用中的重要技术。然而，传统的算法通常丢失几何信息并且没有动态的扩展结构。本文提出了一种基于共形几何代数的新的简化算法。首先，通过球形树构造多分辨率细分，该树形是借助K-means聚类计算最小边界领域，然后采用具有距离计算便利性的完全优点的2种简化方法来实现自我 - 调整简化。最后，利用原始数据或其他算法的几种比较从可视化实现到参数对比度。结果表明，该算法不仅具有良好的效果，而且还具有良好的效果，还具有良好的效果，还具有整体错误率。

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