基于散乱点云的孔洞边界特征点检测

Feature Point Detection of Hole Boundary Based on Scattered Point Cloud

针对模型中存在距离真实孔洞边界点较近的伪边界点情况,大多孔洞识别算法并不能提取完整边界点集合且易出现边界点误识别现象,提出一种融合一阶张量和二阶张量投票算法的孔洞边界特征点检测方法。首先通过建立新衰减函数实现在邻域范围内的一阶棒张量投票的边界点初步提取;再对二阶棒、板、球三种张量相加得到的半正定矩阵进行特征值与显著性的对应关系分析,进一步提取孔洞边界点;最后将两次提取到的边界点集取并集,同时进行噪点的去除,进而达到孔洞边界点检测的目的。试验结果表明,该方法可有效排除伪边界点对孔洞边界特征提取的影响,能够达到较为理想的检测效果,且对噪声较为鲁棒,具有较低的算法复杂度。

In view of the fact that there are pseudo boundary points close to the real hole boundary points in the model, the large hole recognition algorithm cannot extract the complete boundary point set and is prone to misrecognition of boundary points. A fusion of first-order tensor and second-order tensor is proposed. Hole boundary feature point detection method based on voting algorithm. First, establish a new attenuation function to achieve the preliminary extraction of the boundary points of the first-order rod tensor voting in the neighborhood;then perform the eigenvalue and saliency of the semi-definite matrix obtained by the addition of the second-order rod, board, and ball tensors correspondence analysis, further extract the boundary points of the hole;finally, the boundary point set extracted twice is combined, and the noise is removed at the same time to achieve the purpose of detecting the boundary point of the hole. Experimental results show that this method can effectively eliminate the influence of false boundary points on the extraction of hole boundary features, can achieve a more ideal detection effect, is more robust to noise, and has a low algorithm complexity.