  Multi-agent collaborative perception (MCP) has recently attracted much
attention. It includes three key processes: communication for sharing,
collaboration for integration, and reconstruction for different downstream
tasks. Existing methods pursue designing the collaboration process alone,
ignoring their intrinsic interactions and resulting in suboptimal performance.
In contrast, we aim to propose a Unified Collaborative perception framework
named UMC, optimizing the communication, collaboration, and reconstruction
processes with the Multi-resolution technique. The communication introduces a
novel trainable multi-resolution and selective-region (MRSR) mechanism,
achieving higher quality and lower bandwidth. Then, a graph-based collaboration
is proposed, conducting on each resolution to adapt the MRSR. Finally, the
reconstruction integrates the multi-resolution collaborative features for
downstream tasks. Since the general metric can not reflect the performance
enhancement brought by MCP systematically, we introduce a brand-new evaluation
metric that evaluates the MCP from different perspectives. To verify our
algorithm, we conducted experiments on the V2X-Sim and OPV2V datasets. Our
quantitative and qualitative experiments prove that the proposed UMC greatly
outperforms the state-of-the-art collaborative perception approaches.
