Large-scale 3D object segmentation employing conditional angular clustering technique

This paper aims to develop an automatic 3D object segmentation method for the large-scale point clouds. Given a range image, the preprocessing is first applied to get the optimal 3D point cloud. A k-nearest neighbor is built, and a segmentation algorithm based on the conditional angular clustering technique is used to segment the objects from the point cloud. The algorithm is tested on the real point cloud datasets. The experiment results demonstrated that the developed segmentation method can be used to localize the object with the relative uncertainty of 0.27%.