CNN-based 3D object classification using Hough space of LiDAR point clouds

• Published in: Human-centric computing and information sciences 2020-05, Vol.10 (1), Article 19
• Main Authors: Song, Wei, Zhang, Lingfeng, Tian, Yifei, Fong, Simon, Liu, Jinming, Gozho, Amanda
• Format: Article
• Language: English
• Subjects: Accumulators; Algorithms; Artificial Intelligence; Artificial neural networks; Classification; Cloud computing; Communications Engineering; Computer Science; Computer Systems Organization and Communication Networks; Hough transformation; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Labeling; Libraries; Lidar; Networks; Neural networks; Three dimensional models; User Interfaces and Human Computer Interaction
• ISSN: 2192-1962; 2192-1962
• DOI: 10.1186/s13673-020-00228-8

With the wide application of Light Detection and Ranging (LiDAR) in the collection of high-precision environmental point cloud information, three-dimensional (3D) object classification from point clouds has become an important research topic. However, the characteristics of LiDAR point clouds, such as unstructured distribution, disordered arrangement, and large amounts of data, typically result in high computational complexity and make it very difficult to classify 3D objects. Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. The accumulator count in each grid is then computed and input to a CNN model to classify 3D objects. In addition, a semi-automatic 3D object labeling tool is developed to build a LiDAR point clouds object labeling library for four types of objects (wall, bush, pedestrian, and tree). After initializing the CNN model, we apply a dataset from the above object labeling library to train the neural network model offline through a large number of iterations. Experimental results demonstrate that the proposed method achieves object classification accuracy of up to 93.3% on average.