在点云去噪处理过程中,为提高对曲率变化较大区域的去噪效果,提出基于曲率约束的点云分割去噪方法。该方法通过曲率约束点云数据使模型特征得到有效保持,构建噪声光顺的映射函数使得噪声点回归光顺,能够避免使用传统滤波对点云数据模型产生过光顺,对后续处理奠定一定基础。实验表明,该方法相对于双边滤波能够有效地保持模型的特征,保留模型边缘信息,去除噪声效果更为明显。
Abstract
In the process of point cloud de-noising, point cloud segmentation de-noising method based on curvature constraint was proposed in order to improve the de-noising effect of the region with larger curvature change. In the mentioned method, curvature constrained point cloud data was used to keep the model features effectively, and noise smoothing mapping function was constructed to make the noise points regress smoothly, which could avoid over-smoothing the point cloud data model by traditional filtering and lay a foundation for subsequent processing. Experiments showed that the above method could effectively preserve the characteristics of the model, retain the edge information of the model, and the de-noising effect was more obvious than bilateral filtering.
关键词
计量学 /
点云去噪 /
曲率约束 /
映射 /
回归光顺
Key words
metrology /
point cloud de-noising /
curvature constraint /
mapping /
regression smoothing
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基金
国家自然科学基金(51675469);河北省高等学校科学技术研究项目(Z2017040);河北省自然科学基金青年项目(E2018415004)
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