自适应邻域尺寸选择的点云法向量估计算法

Normal estimate method of point clouds based on adaptive neighbor size

三维空间中的法向量估计在计算机视觉和表面重建等研究领域中具有重要的意义,基于局部表面拟合的方法是基于点云数据的经典估计方法。为了增强该方法对于不同局部邻域细节尺度的适应性以得到更准确的估计结果,提出了一种基于自适应邻域尺寸选择的点云法向量估计算法。该方法通过分析三维空间点的邻域点在点的梯度上投影来估计点云中各点的邻域分布情况;最后根据不同的分布情况选择不同的邻域大小,根据该邻域范围内的点拟合出的平面求解得到各点的法向矢量。实验结果表明:该方法能够克服邻域半径选择过大或者过小的情况,有效地提高基于局部表面拟合法向矢量求解的正确性。

Normal vector estimation in three-dimensional space is of great significance in the field of research in computer vision and surface reconstruction, the local surface fitting method is a classical estimation method of point cloud data. In order to improve the veracity of the normal vector which computed by the way of local surface fitting, a method based on optimal neighborhood size for normals estimation was described and analyzed in this paper. The distribution of the neighbor of every point was formulated on the basis of the projection of gradient. Then, the adaptive size was chosen based on the distribution of the neighbor, the normal vector was fitted by the adaptive size. Experimental results show that presented algorithm could avoid the radius of neighbor estimated too large or too small, improve the veracity of the normal vector which computed by the way of local surface fitting effectively.