  In autonomous driving, Vehicle-Infrastructure Cooperative 3D Object Detection
(VIC3D) makes use of multi-view cameras from both vehicles and traffic
infrastructure, providing a global vantage point with rich semantic context of
road conditions beyond a single vehicle viewpoint. Two major challenges prevail
in VIC3D: 1) inherent calibration noise when fusing multi-view images, caused
by time asynchrony across cameras; 2) information loss when projecting 2D
features into 3D space. To address these issues, We propose a novel 3D object
detection framework, Vehicles-Infrastructure Multi-view Intermediate fusion
(VIMI). First, to fully exploit the holistic perspectives from both vehicles
and infrastructure, we propose a Multi-scale Cross Attention (MCA) module that
fuses infrastructure and vehicle features on selective multi-scales to correct
the calibration noise introduced by camera asynchrony. Then, we design a
Camera-aware Channel Masking (CCM) module that uses camera parameters as priors
to augment the fused features. We further introduce a Feature Compression (FC)
module with channel and spatial compression blocks to reduce the size of
transmitted features for enhanced efficiency. Experiments show that VIMI
achieves 15.61% overall AP_3D and 21.44% AP_BEV on the new VIC3D dataset,
DAIR-V2X-C, significantly outperforming state-of-the-art early fusion and late
fusion methods with comparable transmission cost.
