Quasi-Optimal Mesh Segmentation Via Region Growing/Merging

Abstract

Recently meshes of engineering objects are easily acquired by 3D laser or high energy X-ray CT scanning systems, and these meshes are widely used in product developments. To effectively use scanned meshes in engineering applications, such as inspection, CAD model reconstruction, and convergent-type CAE, we need to segment meshes and extract desirable regions and their approximating surfaces as preprocessing. Engineering objects are commonly represented as a set of analytic surfaces, such as planes, cylinders, spheres, cones, and tori. Therefore, the mesh surface of engineering objects needs to be approximated as a set of analytic surfaces. Moreover, a mesh surface should be approximated with a minimum number of analytic surfaces and their approximating error should be minimized as a result of segmentation. We call the segmentation that satisfies these two conditions the optimal segmentation as proposed in [1]. However, optimal segmentation algorithms need a long calculation time. Today’s high energy X-ray CT scanning systems generate large meshes with millions of triangles from objects including hundreds of regions. Thus, computationally expensive algorithms, such as [1], cannot be directly applied to these large and complex meshes from the aspect of efficiency. In this paper we propose an efficient new quasi-optimal mesh segmentation algorithm via region growing and region merging. First, our algorithm robustly and accurately estimates mesh principal curvatures using the local surface fitting by two-pass algorithm. Second, it uses the curvatures to appropriately create seed regions, and then it quickly grows each seed region and extracts grown regions and their approximating analytic surfaces from a whole mesh. Finally, our region merging algorithm efficiently merges extracted regions in order to minimize the number of regions while keeping the user specified tolerances of the surface fitting, and it results in quasi-optimal segmentation. We demonstrate the performance of our algorithm with scanned meshes acquired from real engineering objects by 3D laser and X-ray CT scanning systems.