【摘要】 针对散乱点云模型的边界点检测问题,提出一种通用型的算法。建立目标点的K邻域作为局部参考数据,采用最小二乘法拟合K邻域的微切平面,求得目标点与其邻域点在该平面上的投影;计算目标点投影点与各个邻域点投影点组成的向量,将其等效为合力的各个分力,赋予其反距离权重,加权相加后得到合力,根据距离计算点云密度;利用加权等效合力和密度组成的综合检测参数与阈值比较,判定其是否为边界特征点。实验结果表明,所提算法适用于多种类型的点云模型,具有比传统方法更好的检测效果。更多还原

【Abstract】 Aiming at the problems of boundary detection existing in scattered point cloud model,ageneralized algorithm was proposed.The K-neighborhood of the point cloud was chosen as the local reference data,the tiny tangency plane of the K-neighborhood was established using the least squares method to obtain the projection of the target point and its neighborhood points on the tiny tangency plane.The vector obtained through these projection of points was calculated,which was equivalent to each component force of the resultant force.Each component force was given to the summation of weighted component force by inverse distance weight.According to the distance,the density of the point cloud was calculated.Compared with the threshold,a comprehensive parameter composed of weighted equivalent force and density was wsed to determin whether the points were the boundary points.Experimental results show that the proposed algorithm is suitable for multiple types of the cloud point model,and it can achieve better extraction effect than the traditional methods.更多还原