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.