Collaborative perception leverages rich visual observations from multiple
robots to extend a single robot's perception ability beyond its field of view.
Many prior works receive messages broadcast from all collaborators, leading to
a scalability challenge when dealing with a large number of robots and sensors.
In this work, we aim to address \textit{scalable camera-based collaborative
perception} with a Transformer-based architecture. Our key idea is to enable a
single robot to intelligently discern the relevance of the collaborators and
their associated cameras according to a learned spatial prior. This proactive
understanding of the visual features' relevance does not require the
transmission of the features themselves, enhancing both communication and
computation efficiency. Specifically, we present ActFormer, a Transformer that
learns bird's eye view (BEV) representations by using predefined BEV queries to
interact with multi-robot multi-camera inputs. Each BEV query can actively
select relevant cameras for information aggregation based on pose information,
instead of interacting with all cameras indiscriminately. Experiments on the
V2X-Sim dataset demonstrate that ActFormer improves the detection performance
from 29.89% to 45.15% in terms of AP@0.7 with about 50% fewer queries,
showcasing the effectiveness of ActFormer in multi-agent collaborative 3D
object detection.

本研究提出了一种基于 Transformer 的体系结构，旨在处理依靠摄像机协同感知的扩展性问题。该体系结构通过智能地辨别协作者及其相关摄像机的关联性，以学习的空间先验来主动理解视觉特征的相关性，从而提高通信和计算效率，实现多智能体协同三维物体检测的性能改进。