A New Literature Review of 3D Object Detection on Autonomous Driving

In recent years, the realm of computer vision has experienced a significant surge in the importance of 3D object detection, especially in the context of autonomous driving. The capability to precisely identify the locations, dimensions, and types of key 3D objects surrounding an autonomous vehicle is crucial, rendering 3D object detection a vital component of any advanced perception system. This review delivers an extensive overview of the emerging technologies in 3D object detection tailored for autonomous vehicles. It encompasses a thorough examination, evaluation, and integration of the current research landscape in this domain, staying up-to-date with the latest advancements in 3D object detection and suggesting prospective avenues for future research. Our survey begins by clarifying the principles of 3D object detection and addressing its present challenges in the 3D domain. We then introduce three distinct taxonomies: camera-based, point cloud-based, and multi-modality-based approaches, providing a comprehensive classification of contemporary 3D object detection methodologies from various angles. Diverging from previous reviews, this paper also highlights and scrutinizes common issues and solutions for specific scenarios (such as pedestrian detection, lane lines, roadside cameras, and weather conditions) in object detection. Furthermore, we conduct an in-depth analysis and comparison of different classifications and methods, utilizing various datasets and experimental outcomes. Conclusively, we suggest several potential research directions, offering valuable insights for the ongoing evolution of 3D object detection technology. This review aims to serve as a comprehensive resource for researchers and practitioners in the field, guiding future innovations in 3D object detection for autonomous driving.

Duke Scholars

Author Xin Li Electrical and Computer Engineering