Splitting and Merging Based Multi-model Fitting for Point Cloud Segmentation

This paper deals with the massive point cloud segmentation processing technology on the basis of machine vision, which is the second essential factor for the intelligent data processing of three dimensional conformation in digital photogrammetry. In this paper, multi-model fitting method is used to segment the point cloud according to the spatial distribution and spatial geometric structure of point clouds by fitting the point cloud into different geometric primitives models. Because point cloud usually possesses large amount of 3D points, which are uneven distributed over various complex structures, this paper proposes a point cloud segmentation method based on multi-model fitting. Firstly, the pre-segmentation of point cloud is conducted by using the clustering method based on density distribution. And then the follow fitting and segmentation are carried out by using the multi-model fitting method based on split and merging. For the plane and the arc surface, this paper uses different fitting methods, and finally realizing the indoor dense point cloud segmentation. The experimental results show that this method can achieve the automatic segmentation of the point cloud without setting the number of models in advance. Compared with the existing point cloud segmentation methods, this method has obvious advantages in segmentation effect and time cost, and can achieve higher segmentation accuracy. After processed by method proposed in this paper, the point cloud even with large-scale and complex structures can often be segmented into 3D geometric elements with finer and accurate model parameters, which can give rise to an accurate 3D conformation.