Region Growing Based on 2-D–3-D Mutual Projections for Visible Point Cloud Segmentation

In recent years, with the rapid development of multisensor fusion technology, point clouds used are no longer limited to those including 3-D coordinates acquired by visual sensors, such as binocular sensors or structured light sensors. The 4-D or more multidimensional data are needed to analyze information in a more intuitive way. The 3-D point cloud and 2-D image have complementary information, and the point cloud can be colored by the fusion of coordinate data and intensity data. However, due to the limitation of sight occlusion, only some points in the point cloud are visible in a single image, i.e., they have intensity information. Most existing methods rely on surface reconstruction, which has always been a complex problem in theory and implementation. In this article, a new algorithm named region growing based on 2-D–3-D mutual projections is proposed. Based on the idea of regional growing, we select the initial seed points by the geometric information of the point cloud in the 3-D space and projection plane, and then estimate the visibility of each point according to the growth criteria defined by us. The results show that the proposed method successfully divides the visible points and the occlusion points and achieves satisfactory results in the subsequent intensity fusion. Our method is more robust than the large curvature change and large density change regions. For the car model, the false negative rate of our algorithm decreases by 4.4% compared with Katz’s method, and the score of our algorithm is 15.4% higher than that of Biasutti
et al
.’s method.