A comparative study of noise elimination algorithms for a 3D terrain model through object clustering and the differential method

The technology to automatically detect the surrounding working environment for modeling of the result is the key essential technique for the automation of the construction equipment developments. When noise takes place during 3D modeling of the front work area of the automated construction equipments, the point of noise occurrence during the surface modeling can result in ground level phenomenon in the form of triangular pyramid to lower the quality of the modeling. This can greatly affect the detection of the objects around the automated equipment. This study proposed revised object clustering and differential algorithms for noise elimination of 3D terrain model and compared the noise elimination performance of existing algorithm and proposed algorithm on the images of actual earthwork working environment. It is expected that the noise elimination algorithm proposed in this study will be very useful as a widely used essential technology required for the development of automated technology not only in earthwork field but also in other general construction and civil engineering fields by noise elimination of the 3D working environment model.

[1]  Ross T. Whitaker,et al.  Geometric surface smoothing via anisotropic diffusion of normals , 2002, IEEE Visualization, 2002. VIS 2002..

[2]  Seung-Woo Han,et al.  Development of the Noise Elimination Algorithm of Stereo-Vision Images for 3D Terrain Modeling , 2009 .

[3]  Gengfeng Wu,et al.  3D triangle mesh smoothing via adaptive MMSE filtering , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[4]  Alexander G. Belyaev,et al.  Mesh median filter for smoothing 3-D polygonal surfaces , 2002, First International Symposium on Cyber Worlds, 2002. Proceedings..

[5]  Taeg-Keun Whangbo,et al.  Noise smoothing using the 2D/3D magnitude ratio of mesh data , 2009 .

[6]  Hans-Peter Seidel,et al.  Interactive multi-resolution modeling on arbitrary meshes , 1998, SIGGRAPH.

[7]  Suk-Kyo Hong,et al.  Obstacle Detection using Laser Scanner and Vision System for Path Planning on Autonomous Mobile Agents , 2008 .

[8]  Ki-Doo Kim,et al.  Structure Extraction in 3D Cloud Points Using Color Information and Hough Transform , 2009 .

[9]  Gabriel Taubin,et al.  A signal processing approach to fair surface design , 1995, SIGGRAPH.

[10]  Yutaka Ohtake,et al.  Mesh regularization and adaptive smoothing , 2001, Comput. Aided Des..

[11]  Mark Meyer,et al.  Implicit fairing of irregular meshes using diffusion and curvature flow , 1999, SIGGRAPH.

[12]  D. A. Field.  Laplacian smoothing and Delaunay triangulations , 1988 .

[13]  M. Whitehorn,et al.  Stereo vision in LHD automation , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[14]  Fuyan Zhang,et al.  Anisotropic Feature-Preserving Smoothing of 3D Mesh , 2005, CGIV.