Classification of Point Cloud Segments for Reverse Engineering Using Deep Neural Networks

Abstract

Reverse engineering, defined in this study as obtaining a geometric three-dimensional (3D) computer-aided design (CAD) model from point cloud data, is often used in the manufacturing industry. However, it is necessary to predict the classes of primitive shapes for all the segments in a point cloud in order to generate the 3D CAD model surfaces needed, which is the primary factor driving the increase in required man-hours required for reverse engineering. Herein, we propose a simple deep neural network (DNN) that has relatively low temporal and spatial computational complexity in comparison to other models that can be used to classify point cloud segments. We then show how we trained our model on a dataset of partial point clouds containing primitive shapes to produce several point cloud size and dataset size combinations capable of achieving classification accuracy levels of over 90%. Our experimental results show that it is possible to classify point cloud segments with a small artificial dataset for use in reverse engineering processes.