Curve and surface reconstruction from points: an approach based on self-organizing maps
Curve and surface reconstruction from points: an approach based on self-organizing maps
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从点重建曲线和曲面：一种基于自组织映射的方法
DOI：
10.1016/j.asoc.2004.04.003
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
S. Dhande
中科院分区：
文献类型：
--
作者：
G. Kumar;P. Kalra;S. Dhande
从点云为自由形状物体建模是一种新兴的趋势。从测量点数据中识别形状是将离散数据集转换为分段光滑、连续模型的关键步骤。形状识别就是找出点之间的拓扑关系，在粗放的点云情况下，这一步既需要细化，也需要排序。提出了一种基于增长自组织映射(GSOM)的曲线曲面分段线性重建方法。经过广泛的实验，已经得到了关于该问题域的自组织映射(SOM)算法参数选择的推论。还提出了一种更好的质量指标来评估和比较曲线和曲面重建领域的各种SOM运行。
Modeling of shapes for free form objects from point cloud is an emerging trend. Recognition of shape from the measured point data is a key step in the process of converting discrete data set into a piecewise smooth, continuous model. Shape recognition is to find the topological relation among the points, and in case of thick unorganized point cloud, the step requires both thinning and ordering. The present paper outlines a new approach based on growing self-organizing maps (GSOM) for piecewise linear reconstruction of curves and surfaces from unorganized thick point data. Inferences on selection of self-organizing map (SOM) algorithm parameters for this problem domain have been derived after extensive experimentation. A better quality measure to evaluate and compare various runs of SOM for the domain of curve and surface reconstruction has also been presented.