An improved method for segmentation of point cloud using Minimum Spanning Tree

Abstract

With the development of low-cost 3D sensing hardware such as the Kinect, three dimensional digital images have become popular in medical diagnosis, robotics etc. One of the difficult task in image processing is image segmentation. The problem become simpler if we add the depth channel along with height and width. The proposed algorithm uses Minimum Spanning Tree (MST) for the segmentation of point cloud. As a pre processing step, first level clustering is done which gives group of cluttered objects. Each of this cluttered group is subjected to more finite level of segmentation using MST based on distance and normal. In our method, we build a weighted planar graph of each of the clustered cloud and construct the MST of the corresponding graph. By taking the advantage of normal, we can separate the surface from the object. The proposed method is applied to different 3D scenes and the results are discussed.