A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving

• Published in: Computers & graphics Vol. 99; pp. 153 - 181
• Main Authors: Zamanakos, Georgios, Tsochatzidis, Lazaros, Amanatiadis, Angelos, Pratikakis, Ioannis
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2021; Elsevier Science Ltd
• Subjects: 3D Object detection; Autonomous driving; Data analysis; Deep learning; Lidar; Object recognition; Deep learning; 3D Object detection; Autonomous driving
• ISSN: 0097-8493; 1873-7684
• DOI: 10.1016/j.cag.2021.07.003

•A comprehensive survey of LIDAR-based 3D object detection methods with Deep learning for autonomous driving•Presentation of a common operational pipeline that sets the base for a structured categorisation which facilitates comparison and emerges the similarities and dissimilarities among the presented methods.•Presentation of an exhaustive up-to-date list of 3D object detectors. The method’s description focuses on the particularities of each method with respect to the operational pipeline identifying pros and cons towards an effective and efficient detection outcome.•Fruitful discussion that aims to identify key features which should be either adopted or avoided in the design of new 3D object detectors. [Display omitted] LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing features like insensitivity to light and capacity to capture the 3D spatial structure of an object along with the continuous reduction of its purchase cost. Furthermore, the emergence of Deep Learning as the means to boost performance in 3D data analysis stimulated the production of a multitude of solutions for LIDAR-based 3D object detection which followed different approaches in an effort to respond effectively to several challenges. In view of this, this paper presents a comprehensive survey of LIDAR-based 3D object detection methods wherein an analysis of existing methods is addressed by taking into account a new categorisation that relies upon a common operational pipeline which describes the end-to-end functionality of each method. We next, discuss the existing benchmarking frameworks and present the performance achieved by each method in each of them. Finally, a discussion is presented that provides key insights aiming to capture the essence of current trends in LIDAR-based 3D object detection.