CVFusion: Cross-View Fusion of 4D Radar and Camera for 3D Object Detection
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
4D radar has received significant attention in autonomous driving thanks to
its robustness under adverse weathers. Due to the sparse points and noisy
measurements of the 4D radar, most of the research finish the 3D object
detection task by integrating images from camera and perform modality fusion in
BEV space. However, the potential of the radar and the fusion mechanism is
still largely unexplored, hindering the performance improvement. In this study,
we propose a cross-view two-stage fusion network called CVFusion. In the first
stage, we design a radar guided iterative (RGIter) BEV fusion module to
generate high-recall 3D proposal boxes. In the second stage, we aggregate
features from multiple heterogeneous views including points, image, and BEV for
each proposal. These comprehensive instance level features greatly help refine
the proposals and generate high-quality predictions. Extensive experiments on
public datasets show that our method outperforms the previous state-of-the-art
methods by a large margin, with 9.10% and 3.68% mAP improvements on
View-of-Delft (VoD) and TJ4DRadSet, respectively. Our code will be made
publicly available.
