Abstract
Presented in this paper is a procedure enabling the direct extraction of a simplified triangular mesh from a range image. Although there have been many existing algorithms for simplifying geometric models, they can not be applied to a range image, because most of them have been developed in the context of triangular meshes. The proposed simplification algorithm works directly on a range image, and it is not necessary to convert a range image into a triangular mesh. In developing such a simplification algorithm, the major challenge is handling the topology changes caused by the edge contractions. The key idea of the paper is to employ an additional data structure, called a ‘flag map’, for the support of the irregular topology changes that happen during the simplification procedure. The proposed algorithm has been implemented, and test with various examples.
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Reference
Choi BK, Jerard RB (1998) Sculptured surface machining - theory and applications. Kluwer, Dordrecht
Lee SJ, Chang DY (2007) A laser sensor with multiple detectors for freeform surface digitization. Int J Adv Manuf Technol 31:1181–1190
Son S, Park H, Lee K (2002) Automated laser scanning system for reverse engineering and inspection. Int J Mach Tools Manuf 42:889–897
Valkenburg RJ, Mcivor AM (1998) Accurate 3D measurement using a structured light system. Image Vision Comput 16:99–110
Malassiotis S, Strintzis MG (2005) Robust real-time 3D head pose estimation from range data. Pattern Recogn 38:1153–1165
Min J, Bowyer KW (2005) Improved range image segmentation by analyzing surface fit patterns. Comput Vis Image Und 97:242–258
Chang IS, Park RH (2003) Range image reconstruction based on robust multiresolution estimation of surface parameters. Comput Vis Image Und 24:1123–1131
Balmelli L, Liebling T, Vetterli M (2003) Computational analysis of mesh simplification using global error. Comp Geom-Theor Appl 25:171–196
Kim NH, Yoo SK, Lee KS (2003) Polygon reduction of 3D objects using Stokes’ theorem. Comput Meth Prog Bio 71:203–210
Alvarez R, Noguera JV, Tortosa L, Zamora A (2007) A mesh optimization algorithm based on neural networks. Inform Sciences 177:5347–5364
Li WD, Cai YL, Lu WF (2007) A 3D simplification algorithm for distributed visualization. Comput Ind 58:211–226
Lee KH, Woo H, Suk T (2001) Data reduction methods for reverse engineering. Int J Adv Manuf Technol 17:735–743
Hoppe H (1996) Progressive meshes. In Proc SIGGRAPH 96:99–108
Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W (1993) Mesh optimization. In Proc SIGGRAPH 93:19–26
Schroeder WJ, Zarge JA, Lorensen WE (1992) Decimation of triangle meshes. Comput Graph 26(3):65–70
Soucy M, Laurendeau D (1996) Multiresolution surface modeling based on hierarchical triangulation. Comput Vis Image Und 63(1):1–14
Reinhard K (1998) Multiresolution representations for surfaces meshes based on the vertex decimation method. Comput Graphics 22(1):13–26
Heckbert PS, Garland M (1999) Optimal triangulation and quadric-based surface simplification. Comp Geom-Theor Appl 14(1–3):49–65
Garland M, Heckbert PS (1997) Surface simplification using quadric error metrics. In Proc SIGGRAPH 97:209–216
Garland M, Heckbert PS (1997) Fast triangular approximation of terrains and height fields http://www.cs.berkeley.edu/~jrs/mesh/present.html
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Chang, M., Park, S.C. Range data simplification for reverse engineering. Int J Adv Manuf Technol 41, 86–96 (2009). https://doi.org/10.1007/s00170-008-1449-x
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DOI: https://doi.org/10.1007/s00170-008-1449-x