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基于自适应最优邻域的散乱点云降噪技术研究 被引量:16

Study on Scatter Point Cloud Denoising Technology Based on Self-adaptive Optimal Neighborhood
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摘要 在分析已有滤波技术的基础上,计算点的法向矢量和法向局部方差。采用与法向局部方差有关的自适应角度阈值的截断函数限制邻域点的选择,获得与表面特征有关的自适应最优邻域;采用改进的三边滤波方法实现法向矢量滤波和位置滤波。实验验证了该方法的可行性,与其他滤波方法相比,该算法能更有效地保持细节特征,同时获得光顺的离散表面。 After study of existing point cloud denoising algorithm carefully, the normal vector and local square error in normal direction were computed for point sets. A self adaptive optimal denoising neighbor which was related to the surface feature was achieved by using a self adaptive angle threshold trimming function. Normal space denoising and location space denoising were carried out by using improved trilateral filter. A series of experiments work well,contrasting the new denoising algorithm herein and other ways, more features have preserved and smoothing surface have been achieved.
出处 《中国机械工程》 EI CAS CSCD 北大核心 2010年第6期639-643,共5页 China Mechanical Engineering
基金 国家863高技术研究发展计划资助项目(2007AA04Z124) 江苏省科技支撑计划资助项目(BE2008058)
关键词 自适应 最优邻域 散乱点云 降噪 self- adaptive optimal neighborhood scatter point cloud denoising
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参考文献9

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