  Multi-agent collaborative perception has emerged as a widely recognized
technology in the field of autonomous driving in recent years. However, current
collaborative perception predominantly relies on LiDAR point clouds, with
significantly less attention given to methods using camera images. This
severely impedes the development of budget-constrained collaborative systems
and the exploitation of the advantages offered by the camera modality. This
work proposes an instance-level fusion transformer for visual collaborative
perception (IFTR), which enhances the detection performance of camera-only
collaborative perception systems through the communication and sharing of
visual features. To capture the visual information from multiple agents, we
design an instance feature aggregation that interacts with the visual features
of individual agents using predefined grid-shaped bird eye view (BEV) queries,
generating more comprehensive and accurate BEV features. Additionally, we
devise a cross-domain query adaptation as a heuristic to fuse 2D priors,
implicitly encoding the candidate positions of targets. Furthermore, IFTR
optimizes communication efficiency by sending instance-level features,
achieving an optimal performance-bandwidth trade-off. We evaluate the proposed
IFTR on a real dataset, DAIR-V2X, and two simulated datasets, OPV2V and V2XSet,
achieving performance improvements of 57.96%, 9.23% and 12.99% in AP@70 metrics
compared to the previous SOTAs, respectively. Extensive experiments demonstrate
the superiority of IFTR and the effectiveness of its key components. The code
is available at https://github.com/wangsh0111/IFTR.
