3-D Urban Objects Detection and Classification From Point Clouds

In this paper, we present our approach to solve the DEBS Grand challenge 2019 which consists of classifying urban objects in different scenes that originate from a LiDAR sensor. In general, at any point in time, LiDAR data can be considered as a point cloud where a reliable feature extractor and a classification model are required to be able to recognize 3-D objects in such scenes. Herein, we propose and describe an implementation of a 3-D point cloud object detection and classification system based on a 3-D global feature called Ensemble of Shape Functions (ESF) and a random forest object classifier.