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首页> 外文期刊>IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics >Models and algorithms for efficient multiresolution topology estimation of measured 3-D range data
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Models and algorithms for efficient multiresolution topology estimation of measured 3-D range data

机译：用于有效测量3D范围数据的多分辨率拓扑估计的模型和算法

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摘要

In this paper, we propose a new efficient topology estimation algorithm to construct a multiresolution polygonal mesh from measured three-dimensional (3-D) range data. The topology estimation problem is defined under the constraints of cognition, compactness, and regularity, and the algorithm is designed to be applied to either a cloud of points or a dense mesh. The proposed algorithm initially segments the range data into a finite number of Voronoi patches using the K-means clustering algorithm. Each patch is then approximated by an appropriate polygonal and eventually a triangular mesh model. In order to improve the equiangularity of the mesh, we employ a dynamic mesh model, in which the mesh finds its equilibrium state adaptively, according to the equiangularity constraint. Experimental results demonstrate that satisfactory equiangular triangular mesh models can be constructed rapidly at various resolutions, while yielding tolerable modeling error.
机译：在本文中，我们提出了一种新的有效拓扑估计算法，该算法可从测量的三维（3-D）范围数据构建多分辨率多边形网格。拓扑估计问题是在认知，紧凑性和规则性的约束下定义的，并且该算法被设计为应用于点云或密集网格。所提出的算法最初使用K均值聚类算法将距离数据分割为有限数量的Voronoi面片。然后，通过适当的多边形网格并最终通过三角形网格模型对每个面片进行近似。为了提高网格的等角性，我们采用了动态网格模型，其中，网格根据等角性约束自适应地找到其平衡状态。实验结果表明，可以在各种分辨率下快速构建令人满意的等角三角形网格模型，同时产生可容忍的建模误差。

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