Constructing NURBS surface model from scattered and unorganized range data

I. Park, Sang Uk Lee, I. Yun
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引用次数: 51

Abstract

We propose an algorithm to produce a 3D surface model from a set of range data, based on the Non-Uniform Rational B-Splines (NURBS) surface fitting technique. It is assumed that the range data is initially unorganized and scattered 3D points, while their connectivity is also unknown. The proposed algorithm is roughly made up of two stages: initial model approximation employing K-means clustering, and construction of NURBS patch network using hierarchical graph representation. The initial model is approximated by both a polyhedral and triangular model. Then, the initial model is represented by a hierarchical graph, which is efficiently used to construct the G/sup 1/ continuous NURBS patch network of the whole object. Experiments are carried out on synthetic and real range data to evaluate the performance of the proposed algorithm. It is shown that the initial model, as well as the NURBS patch network, are constructed automatically, while the modeling error is observed to be negligible.
利用离散无组织距离数据构建NURBS曲面模型
我们提出了一种基于非均匀有理b样条(NURBS)曲面拟合技术,从一组距离数据生成三维曲面模型的算法。假设距离数据最初是无组织的、分散的3D点,它们的连通性也是未知的。该算法大致分为两个阶段:采用K-means聚类的初始模型逼近和采用分层图表示的NURBS补丁网络构建。初始模型近似为多面体模型和三角形模型。然后,用层次图表示初始模型,有效地构建了整个目标的G/sup 1/连续NURBS补丁网络。在合成和真实距离数据上进行了实验,以评估该算法的性能。结果表明,初始模型和NURBS补丁网络是自动构建的,建模误差可以忽略不计。
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