  Multi-view radar-camera fused 3D object detection provides a farther
detection range and more helpful features for autonomous driving, especially
under adverse weather. The current radar-camera fusion methods deliver kinds of
designs to fuse radar information with camera data. However, these fusion
approaches usually adopt the straightforward concatenation operation between
multi-modal features, which ignores the semantic alignment with radar features
and sufficient correlations across modals. In this paper, we present MVFusion,
a novel Multi-View radar-camera Fusion method to achieve semantic-aligned radar
features and enhance the cross-modal information interaction. To achieve so, we
inject the semantic alignment into the radar features via the semantic-aligned
radar encoder (SARE) to produce image-guided radar features. Then, we propose
the radar-guided fusion transformer (RGFT) to fuse our radar and image features
to strengthen the two modals' correlation from the global scope via the
cross-attention mechanism. Extensive experiments show that MVFusion achieves
state-of-the-art performance (51.7% NDS and 45.3% mAP) on the nuScenes dataset.
We shall release our code and trained networks upon publication.
