溶洞表面具有复杂、不规则性,现有的诸多的建模方法都是按照统一分辨率来进行三维重建,但是在三维重建过程中效率很低,且模型文件过大,对后续专业应用造成了很大困难,因此提出了一种基于三维激光点云特征线提取的技术,并针对溶洞进行了多分辨率三维重建。首先,采用改进邻近点几何特征提取溶洞特征值,增加法向量角作为检测特征点的参数;其次,用社会粒子群(SPSO)算法与模糊C-均值(FCM)聚类算法实现点云分类;再次,采用折线生长方法将特征点连接成特征线,并将其投影到三维点云上;最后,利用分类后的点云按照不同分辨率建模,实现高精度、高质量、高效率溶洞三维重建。实验结果表明,该方法可以按照不同分辨率进行建模,减少了三维重建后模型的数据量,提高了三维重建效率,在溶洞三维重建方面具有较高的实用价值。

The surfaces of caves are complex and irregular. Many existing modeling methods are based on an overall-resolution 3D reconstruction. Although the overall model resolution is guaranteed, the efficiency of 3D reconstruction is substantially low, and the model file is too large, which makes it considerably difficult for the follow-up professional application. Therefore, a multi-resolution 3D reconstruction technology based on 3D laser point cloud feature line extraction was proposed for the karst cave. First, the enhanced geometric features of neighboring points were used to extract the eigenvalues and increased the normal vector angle as a basis for detecting the feature points. Second, the standard particle swarm optimization(SPSO) and fuzzy C-means clustering algorithms were used to realize the point cloud classification. Third, the broken line growth method was used to connect the feature points into the feature lines and project them onto 3D point cloud; finally, the classified point cloud was used for modeling as per different resolutions. The 3D reconstruction of the karst cave was realized with high precision, high quality, and high efficiency. The experimental results show that this method can improve the efficiency of the 3D reconstruction of karst caves, realize modeling according to different resolutions, reduce the amount of data after the 3D reconstruction, improve the efficiency of 3D reconstruction, and has high practical value.