Developments in 3-D Object Detection for Autonomous Driving: A Review
Yu Wang, Shaohua Wang, Yicheng Li, Mingchun Liu
Abstract
In recent years, 3D object perception has emerged as a critical component in the development of autonomous driving systems, offering essential environmental awareness. As perception tasks become increasingly complex, a variety of detection techniques have been proposed, leading to diverse perspectives from both academia and industry. While numerous surveys exist, they primarily focus on specific detection methods or single-sensor approaches, lacking a broader perspective that analyzes the landscape of 3D object perception across multiple modalities. This review provides a comprehensive and panoramic perspective by systematically summarizing and analyzing 3D object detection methods, encompassing camera-based, LiDAR-based, and multi-sensor fusion techniques. Beyond evaluating the strengths and limitations of these approaches, we examine the critical challenges encountered in real-world applications, such as synchronization issues, calibration drift, and the inherent limitations of sensor fusion. Furthermore, we explore emerging research directions, including temporal perception, 3D occupancy grids, and cooperative perception methods that extend the perception range through collaborative communication. By offering a holistic view of the current progress and future developments in 3D object perception, this review serves as a valuable resource for researchers and practitioners. Additionally, to facilitate continuous updates on the latest advancements in the field, we have established an active repository, accessible at: https://github.com/Fishsoup0/Autonomous-Driving-Perception.