为提高结构光三维重构系统的点云匹配速度及精度,提出二维视图及三维点云交叉特征点协同匹配的方法。首先,通过投影变换及维度映射关系实现待拼接投影图像的归一化,经预处理后提取端点及分叉点作为关键点,对同类点进行三角划分及相似匹配得到初始点集,并将其映射至三维空间。其次,利用kd-tree搜索得到双邻域质心,根据三点构成的三角形相似关系进一步筛选点集。最后,采用四元数法完成粗拼接,进而使用改进的迭代最近点(ICP)算法完成精拼接。实验结果表明,所提算法的匹配准确率达98.16%,匹配用时3s,粗拼接重叠区域的重心距离误差为0.018mm,所提算法对于二维图像视角变换、纹理光滑、光线不均等具有较高的鲁棒性。

In order to improve the speed and accuracy of point cloud matching in the structured light three-dimensional reconstruction system, a collaborative matching method of two-dimensional view and three-dimensional point cloud across feature points is proposed in this work. First, the normalization of the projected images to be spliced is realized through projection transformation and dimension mapping. After preprocessing, the endpoints and bifurcation points are extracted as key points, and the similar points are triangulated and similarly matched to obtain the initial point set. The initial point set is mapped to three-dimensional space. Second, kd-tree search is used to obtain the centroid of the double neighborhood, and the point set is further screened according to the triangle similarity relationship formed by the three points. Finally, the quaternion method is used to complete the rough splicing, and then an improved iterative closest point (ICP) algorithm is used to complete the fine splicing. Experimental results show that the matching accuracy of the proposed algorithm is 98.16%, the matching time is 3 s, and the center of gravity distance error of the coarse splicing overlap area is 0.018mm. The proposed algorithm has high robustness for two-dimensional image perspective transformation, smooth texture, and uneven light.