EMIFF: Enhanced Multi-scale Image Feature Fusion for
  Vehicle-Infrastructure Cooperative 3D Object Detection
Zhe Wang Siqi Fan Xiaoliang Huo Tongda Xu Yan Wang Jingjing Liu Yilun Chen Ya-Qin Zhang
In autonomous driving, cooperative perception makes use of multi-view cameras
from both vehicles and infrastructure, providing a global vantage point with
rich semantic context of road conditions beyond a single vehicle viewpoint.
Currently, two major challenges persist in vehicle-infrastructure cooperative
3D (VIC3D) object detection: $1)$ inherent pose errors when fusing multi-view
images, caused by time asynchrony across cameras; $2)$ information loss in
transmission process resulted from limited communication bandwidth. To address
these issues, we propose a novel camera-based 3D detection framework for VIC3D
task, Enhanced Multi-scale Image Feature Fusion (EMIFF). To fully exploit
holistic perspectives from both vehicles and infrastructure, we propose
Multi-scale Cross Attention (MCA) and Camera-aware Channel Masking (CCM)
modules to enhance infrastructure and vehicle features at scale, spatial, and
channel levels to correct the pose error introduced by camera asynchrony. We
also introduce a Feature Compression (FC) module with channel and spatial
compression blocks for transmission efficiency. Experiments show that EMIFF
achieves SOTA on DAIR-V2X-C datasets, significantly outperforming previous
early-fusion and late-fusion methods with comparable transmission costs.