Point Cloud Reduction Using Support Vector Machines
Abstract
This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned.
Keywords:
Reverse engineering / point cloud reduction / support vector machinesSource:
Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies, 2012, 121-124Collections
Institution/Community
Mašinski fakultetTY - CONF AU - Jakovljević, Živana PY - 2012 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5184 AB - This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned. C3 - Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies T1 - Point Cloud Reduction Using Support Vector Machines EP - 124 SP - 121 UR - https://hdl.handle.net/21.15107/rcub_machinery_5184 ER -
@conference{ author = "Jakovljević, Živana", year = "2012", abstract = "This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned.", journal = "Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies", title = "Point Cloud Reduction Using Support Vector Machines", pages = "124-121", url = "https://hdl.handle.net/21.15107/rcub_machinery_5184" }
Jakovljević, Ž.. (2012). Point Cloud Reduction Using Support Vector Machines. in Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies, 121-124. https://hdl.handle.net/21.15107/rcub_machinery_5184
Jakovljević Ž. Point Cloud Reduction Using Support Vector Machines. in Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies. 2012;:121-124. https://hdl.handle.net/21.15107/rcub_machinery_5184 .
Jakovljević, Živana, "Point Cloud Reduction Using Support Vector Machines" in Proceedings of the 11th International Scientific Conference MMA 2012 Advanced Production Technologies (2012):121-124, https://hdl.handle.net/21.15107/rcub_machinery_5184 .