A Systematic Survey of Transformer-Based 3D Object Detection for Autonomous Driving: Methods, Challenges and Trends
Minling Zhu, Yadong Gong, Chunwei Tian, Zuyuan Zhu
Abstract
In recent years, with the continuous development of autonomous driving technology, 3D object detection has naturally become a key focus in the research of perception systems for autonomous driving. As the most crucial component of these systems, 3D object detection has gained significant attention. Researchers increasingly favor the deep learning framework Transformer due to its powerful long-term modeling ability and excellent feature fusion advantages. A large number of excellent Transformer-based 3D object detection methods have emerged. This article divides the methods based on data sources. Firstly, we analyze different input data sources and list standard datasets and evaluation metrics. Secondly, we introduce methods based on different input data and summarize the performance of some methods on different datasets. Finally, we summarize the limitations of current research, discuss future directions and provide some innovative perspectives.