Original scientific paper

https://doi.org/10.17559/TV-20180328175336

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Veljko Markovic orcid.org/0000-0003-2873-2150 ; University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11000 Belgrade, Serbia
Zivana Jakovljevic orcid.org/0000-0002-7878-2909 ; University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11000 Belgrade, Serbia
Zoran Miljkovic orcid.org/0000-0001-9706-6134 ; University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11000 Belgrade, Serbia

Abstract

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on ε insensitive support vector regression (ε-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of ε-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines.

Keywords

3D scanning; 3D data acquisition; point cloud simplification; support vector regression

Hrčak ID:

223288

URI

https://hrcak.srce.hr/223288

Publication date:

25.7.2019.

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