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散乱数据点的细分曲面重建算法及实现

Surface Reconstruction from Unorganized Points Using Subdivision Techniques

  • 摘要: 提出一种对海量散乱数据根据给定精度拟合出无需裁剪和拼接的、反映细节特征的、分片光滑的细分曲面算法.该算法的核心是基于细分的局部特性,通过对有特征的细分控制网格极限位置分析,按照拟合曲面与数据点的距离误差最小原则,对细分曲面控制网格循环进行调整、优化、特征识别、自适应细分等过程,使得细分曲面不断地逼近原始数据.实例表明:该算法不仅具有高效性、稳定性,同时构造出的细分曲面还较好地反映了原始数据的细节特征.

     

    Abstract: A new approach to reconstruct subdivision surfaces from unorganized points of arbitrary topology is proposed. Reconstructed surfaces are piecewise smooth subdivision surfaces without trimming and sewing. The main idea is to utilize the local characteristic of Loop subdivision to adjust, optimize and adaptive subdivide control mesh to let the subdivision surface fit the data points. Examples demonstrated that the algorithm is efficient and robust, can effectively preserve the detail characteristics of data points.

     

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