Range image segmentation with application to CAD model acquisition

The process of CAD model acquisition from existing objects is desirable in many industrial applications. An automated range image segmentation module is an essential building block and is key to speeding up this process. This paper presents a segmentation method based on local approximation of scan lines and uses adequate edge models to detect noise pixels as well as position and orientation discontinuities. This is followed by an adaptive grouping process to find a geometric representation of the different surface regions of the object. The output of the segmentation module is then used to automatically generate a surface CAD model of the scene. Experimental results on a large number of real range images demonstrate the efficiency and robustness of the method.