Algorithm for Implicit Surface Reconstruction from Point Cloud Data with Noises
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摘要: 针对三维扫描获取的带噪声和离群点的点云数据,提出了隐式曲面重建算法.用基于均值漂移的滤波算子,把每个采样点移动到核密度函数的局部最大值的点,以限制噪声并剔除离群点.然后,用自适应的八叉树空间划分方法将降噪后的采样点数据分成小的子域.最后,在每个子域内计算局部形状函数,并用单位分解法将所有的局部形状函数加权求和以逼近模型的全局函数.实验表明,该方法是鲁棒的,能用带噪声和离群点的点云数据实现多分辨三维重建,得到细节丰富的曲面.Abstract: An algorithm for reconstructing surfaces from point clouds with noises and outliers acquired with a 3D scanner was presented.In the algorithm,each point is shifted using a filtering operator based on mean shift scheme to the local maximum of kernel density function to suppress noises and remove the outliers.The filtered data are divided into subdomains using an adaptive octree subdivision method.A local shape function is computed on each subdomain,and all the local shape functions with their weights are summed up using the method of partition of unity to approximate the global domain.Numerical experiments demonstrate that the proposed method is robust and accurate and convenient in implementing multi-resolution 3D reconstruction from point clouds data containing noises and outliers.
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Key words:
- mean shift /
- space subdivision /
- shape function /
- partition of unity /
- surface reconstruction /
- point cloud /
- noise
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