Abstract
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.
Abstract (translated)
多Agent 协作感知(MCP)最近引起了广泛关注。它包括三个关键过程:沟通分享、协作整合,以及不同下游任务的重建。现有的方法主要致力于设计协作过程,而忽视了其内在相互作用,导致性能最优化。相反,我们旨在提出一个名为UMC的统一协作感知框架,通过多分辨率技术优化沟通、协作和重建过程。沟通引入了一种可训练的多分辨率和选择性区域(MRSR)机制,实现高质量低带宽。然后,我们提出了基于图的协作,在每个分辨率上适应MRSR。最后,重建整合了多分辨率协作特征,以下游任务为例。由于通用指标无法系统地反映 MCP 带来的性能增强,我们引入了一个全新的评估指标,从不同角度评估了 UMC。为了验证我们的算法,我们研究了V2X-Sim和OPV2V数据集。我们的定量和定性实验表明,所提出的UMC远远优于当前先进的协作感知方法。
URL
https://arxiv.org/abs/2303.12400
