A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.