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Problem statement: Segmentation of 3D range images is widely used in computer vision as&#13; an essential pre-processing step before the methods of high-level vision can be applied. Segmentation&#13; aims to study and recognize the features of range image such as 3D edges, connected surfaces and&#13; smooth regions. Approach: This study presents new improvements in segmentation of terrestrial 3D&#13; range images based on edge detection technique. The main idea is to apply a gradient edge detector in&#13; three different directions of the 3D range images. This 3D gradient detector is a generalization of the&#13; classical sobel operator used with 2D images, which is based on the differences of normal vectors or&#13; geometric locations in the coordinate directions. The proposed algorithm uses a 3D-grid structure&#13; method to handle large amount of unordered sets of points and determine neighborhood points. It&#13; segments the 3D range images directly using gradient edge detectors without any further computations&#13; like mesh generation. Our algorithm focuses on extracting important linear structures such as doors,&#13; stairs and windows from terrestrial 3D range images these structures are common in indoors and&#13; outdoors in many environments. Results: Experimental results showed that the proposed algorithm&#13; provides a new approach of 3D range image segmentation with the characteristics of low&#13; computational complexity and less sensitivity to noise. The algorithm is validated using seven&#13; artificially generated datasets and two real world datasets. Conclusion/Recommendations:&#13; Experimental results showed that different segmentation accuracy is achieved by using higher Grid&#13; resolution and adaptive threshold

A New Approach for 3D Range Image Segmentation using Gradient Method