一种基于局部坐标系变换和单位分割的有效曲面重构方法

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhenghua Zhou, Yanqing Fu, Jianwei Zhao
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引用次数: 2

摘要

径向基函数(RBF)由于其较强的逼近能力,被广泛应用于离散三维点数据的表面重建。然而,在确定参数时,通常需要在约束条件中加入非地表点等附加信息,这显然增加了计算成本和数据的不可靠性。为了避免增加额外的离面点约束,提出了一种基于局部坐标系变换和单位分割的曲面重建方法。首先构造显式RBF函数来逼近局部曲面斑块,然后通过局部系统坐标变换将其转化为等效的隐式曲面重构形式。与局部隐式曲面近似方法相比,本文提出的局部显式曲面近似方法能够避免RBF近似中出现的平凡解,且不增加数据求解的规模。在大型数据集、非均匀性数据集和噪声数据集上,将该方法与传统的基于rbf的方法和多级分割统一(MPU)方法进行了对比实验。实验结果表明,该方法具有较好的鲁棒性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient method for surface reconstruction based on local coordinate system transform and partition of unity
Radial basis function (RBF) has been extensively applied for surface reconstruction from scattered 3D point data due to its strong ability of approximation. However, additional information, such as off-surface points, are usually required to be appended into constraints for determining the parameters, which apparently increases the computation cost and data unreliability. To avoid adding additional off surface point constraints, a novel surface reconstruction approach based on local coordinate system transform and partition of unity is proposed in this paper. Firstly, the explicit RBF functions are constructed to approximate the local surface patches, and then it is transformed into an equivalent implicit surface reconstruction form by local system coordinate transformation. Compared with the local implicit surface approximation, the proposed local explicit surface approximation method is capable of avoiding trivial solution occurred in RBF approximating, and does not increase the scale of data solution. A number of comparison experiments of the proposed method with the traditional RBF-based method and the multi-level partition of unity (MPU) method are carried out on some kinds of large dataset, non-uniformity dataset, noisy dataset. The experimental results illustrate that the proposed method is robust and effective in dealing with large-scale point clouds surface reconstruction.
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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