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中文摘要：
为了提高对点云模型处理的有效性,提出一种对点云模型的法向估计和重定向方法.首先利用基于局部平面拟合的主元分析方法得到初步法向估计;然后改进移动最小二乘曲面实现局部曲面拟合,进一步得到更加准确的法向,实现了点云模型的去噪光顺;最后通过增加切向约束规则来修正法向重定向中的法向传播方向.实验结果表明,对于具有复杂细节(如紧邻面、尖角形状等)的点云模型,该方法可以提高法向计算的准确度,并得到光顺的点云模型.在实际应用中,该方法可以很好地应用于点云模型的预处理,为后续的模型处理和分析提供良好的数据基础.
英文摘要：
The accuracy of normal estimation and normal orientation have great impacts on the processing of point cloud models, such as denoising, registering and surface reconstruction. In terms of normal estimation for 3D point cloud models, firstly the local plane approximation based on PCA method was used to get a preliminary normal estimation. And then the improved Moving Least Squares Surface (MLSS) method was used to get local approximate surface, and thus produce more accurate normals which were resilient to noises. For normal orienta-tion, a new rule of tangential constraint was proposed in the process of normal propagation. Finally, the Poisson surface reconstruction approach was employed to verify the effectiveness of estimated normals. Experimental re-sults show that the accuracy of normal estimation is improved by our method, and smooth models can be obtained as well. Our proposed method can be very useful in the preprocessing of point cloud models.
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