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.

In recent years, 3D object detection using neural radiance fields (NeRF) has advanced significantly, yet challenges remain in effectively utilising the density field. Current methods often treat NeRF as a geometry learning tool or rely on volume rendering, neglecting the density field's potential and feature dependencies. To address this, we propose NeRF‐C3D, a novel framework incorporating a multi‐scale feature fusion module with channel attention (MFCA). MFCA leverages channel attention to model feature dependencies, dynamically adjusting channel weights during fusion to enhance important features and suppress redundancy. This optimises density field representation and improves feature discriminability. Experiments on 3D‐FRONT, Hypersim, and ScanNet demonstrate NeRF‐C3D's superior performance validating MFCA's effectiveness in capturing feature relationships and showcasing its innovation in NeRF‐based 3D detection.