UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework
This work addresses performance bottlenecks in multi-agent collaborative perception for applications like autonomous driving, though it appears incremental as it builds on existing methods with optimizations.
The authors tackled the problem of suboptimal performance in multi-agent collaborative perception by proposing UMC, a unified framework that optimizes communication, collaboration, and reconstruction processes using multi-resolution techniques, achieving state-of-the-art results on V2X-Sim and OPV2V datasets.
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.