Learning Geometric Primitives in Point Clouds

Abstract

Primitive recognition in 3D point clouds is an important aspect in reverse engineering. We propose a method for primitive recognition based on machine learning approaches. The machine learning approaches used for the classification are linear discriminant analysis (LDA) and multi-class support vector machines (SVM). For the classification process local geometric properties (features) of the point cloud are computed based on point relations, normals, and principal curvatures. For the training phase point clouds are generated using a simulation of a laser scanning device based on ray tracing with an error model. The classification rates of novel, curvaturebased geometric features are compared to known geometric features to prove the effectiveness of the approach.