ActFormer：基于主动查询的可扩展协同感知
ActFormer: Scalable Collaborative Perception via Active Queries
AI总结：
针对多机器人协同感知的可扩展性问题，提出ActFormer模型，通过BEV查询主动选择相关相机聚合信息，在V2X-Sim数据集上以更少查询显著提升3D目标检测性能。
摘要：
协同感知利用多机器人的丰富视觉观测，将单机器人的感知能力拓展至其视野范围之外。现有多数工作接收所有协作方广播的消息，在处理大量机器人与传感器时面临可扩展性挑战。本文旨在通过基于Transformer的架构，解决基于相机的可扩展协同感知问题。核心思路是让单机器人依据学习到的空间先验，智能判别协作方及其关联相机的相关性。这种对视觉特征相关性的主动理解无需传输特征本身，可同时提升通信与计算效率。具体而言，我们提出ActFormer——一种Transformer模型，通过预定义的鸟瞰图（BEV）查询与多机器人多相机输入交互，学习BEV表征。每个BEV查询可基于位姿信息主动选择相关相机进行信息聚合，而非无差别地与所有相机交互。在V2X-Sim数据集上的实验表明，ActFormer将AP@0.7指标下的检测性能从29.89%提升至45.15%，同时查询数量减少约50%，彰显了其在多智能体协同3D目标检测中的有效性。
英文摘要：
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.
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